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First look at $NVDA photonics co-packaged optics switch with Lambda. As GB300 NVL72 scales, networking becomes the layer that determines how fast the cluster thinks while co-packaged optics cut power and improve tokens per watt.

Shay Boloor

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101,623 Aufrufe • vor 1 Monat •via X (Twitter)

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$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

TheValueist

76,179 Aufrufe • vor 7 Monaten

I said earlier in the week that $MSFT could be the catalyst that reminded investors the AI buildout was nowhere near over. That is exactly what happened as Microsoft’s June fiscal year-end gave the first major look into first half of 2027 spending and confirmed demand remains above capacity across chips, memory, networking, optics, packaging, power and equipment. Then this morning we learned why the suppliers sold off since Leopold Aschenbrenner’s Situational Awareness ran ~$24B at up to 4x leverage with $NBIS, $SNDK, $CRWV, $IREN, $BE and $TE among its largest positions and was forced to dump its entire public book in one block. The AI infrastructure thesis never broke but the leveraged fund behind the selling did.

I said earlier in the week that $MSFT could be the catalyst that reminded investors the AI buildout was nowhere near over. That is exactly what happened as Microsoft’s June fiscal year-end gave the first major look into first half of 2027 spending and confirmed demand remains above capacity across chips, memory, networking, optics, packaging, power and equipment. Then this morning we learned why the suppliers sold off since Leopold Aschenbrenner’s Situational Awareness ran ~$24B at up to 4x leverage with $NBIS, $SNDK, $CRWV, $IREN, $BE and $TE among its largest positions and was forced to dump its entire public book in one block. The AI infrastructure thesis never broke but the leveraged fund behind the selling did.

Shay Boloor

242,024 Aufrufe • vor 8 Tagen

Dylan Patel believes $NVDA will not use CPO even with Feynman “We’re very bullish on copper and non-CPO optics. We're kinda bearish CPO. $APH is going to perform much better over the next few years than previously expected.” Full quote: “The spending associated with AI chips is currently at or below 10%, but when we move to CPO, networking will grow even further, to 20% or 30%. We are seeing a huge uplift in networking content. CPO is a massive step-function change for the industry, and everyone recognizes it now, but people are getting a little too exuberant. In my view, it is not coming in 2027. It will really arrive toward the end of 2028, with 2029 being the real ramp for scale-up CPO. There have been a lot of manufacturing problems. If we could deploy it today at a good cost, everyone would do it. However, it is really hard. The manufacturing volumes and yields are not there, and the chips are not fully designed for it yet. It is a very complex and difficult technology to ramp, so people are going to stay with copper for as long as they can. That means Rubin is all copper, and Feynman, the next-generation Nvidia GPU after Rubin and Rubin Ultra, is still copper. We are not even shipping Rubin yet. We have a few generations of chips before we get to CPO on the GPU. CPO on switches is coming earlier than on GPUs or AI ASICs. Even without CPO, as cluster sizes get bigger, you need more optics, active electrical cables, and similar components per GPU. We have seen a major shift in the dynamics. On Monday, we released a note for our institutional research subscribers stating that, over the medium term, we are actually very bullish on copper and non-CPO optics, while being slightly bearish on CPO. This is due to delays in downstream chips, such as Feynman not fully utilizing CPO. Copper companies like Amphenol, which make backplane connectors and cables, are going to perform much better over the next few years than previously expected because CPO is being delayed. Ultimately, integrating optics is much more expensive than sending signals electrically. However, electrical signals cannot travel very far without adding repeaters or optics. There is a trade-off continuum, and while CPO will happen, it is currently being pushed out.”

Dylan Patel believes $NVDA will not use CPO even with Feynman “We’re very bullish on copper and non-CPO optics. We're kinda bearish CPO. $APH is going to perform much better over the next few years than previously expected.” Full quote: “The spending associated with AI chips is currently at or below 10%, but when we move to CPO, networking will grow even further, to 20% or 30%. We are seeing a huge uplift in networking content. CPO is a massive step-function change for the industry, and everyone recognizes it now, but people are getting a little too exuberant. In my view, it is not coming in 2027. It will really arrive toward the end of 2028, with 2029 being the real ramp for scale-up CPO. There have been a lot of manufacturing problems. If we could deploy it today at a good cost, everyone would do it. However, it is really hard. The manufacturing volumes and yields are not there, and the chips are not fully designed for it yet. It is a very complex and difficult technology to ramp, so people are going to stay with copper for as long as they can. That means Rubin is all copper, and Feynman, the next-generation Nvidia GPU after Rubin and Rubin Ultra, is still copper. We are not even shipping Rubin yet. We have a few generations of chips before we get to CPO on the GPU. CPO on switches is coming earlier than on GPUs or AI ASICs. Even without CPO, as cluster sizes get bigger, you need more optics, active electrical cables, and similar components per GPU. We have seen a major shift in the dynamics. On Monday, we released a note for our institutional research subscribers stating that, over the medium term, we are actually very bullish on copper and non-CPO optics, while being slightly bearish on CPO. This is due to delays in downstream chips, such as Feynman not fully utilizing CPO. Copper companies like Amphenol, which make backplane connectors and cables, are going to perform much better over the next few years than previously expected because CPO is being delayed. Ultimately, integrating optics is much more expensive than sending signals electrically. However, electrical signals cannot travel very far without adding repeaters or optics. There is a trade-off continuum, and while CPO will happen, it is currently being pushed out.”

Daniel Romero

117,323 Aufrufe • vor 28 Tagen

THE 5 BIGGEST BOTTLENECKS POWERING THE AI ECONOMY The way I'm thinking about AI winners today is that the market is moving beyond the simple question of which mega-cap company spends most on AI and has been rewarding the companies that control the scarce inputs, contracted capacity, data movement, power infrastructure, edge compute and workflow layers that make the AI economy function. These are the five bottlenecks I'm watching most: • Memory | $MU, Samsung, SK Hynix Memory is the clearest scarce input because HBM feeds the accelerator, only a few companies can make it at volume and buyers are locking in supply through long-term agreements that create revenue visibility through the end of the decade. • Connectivity | $AVGO, $MRVL, $ALAB, $CRDO, $AAOI, $ANET Connectivity determines whether AI clusters can move data fast enough because training runs span tens of thousands of chips that need to act like one machine. Once copper runs out of reach that causes the cluster depends on optics, retimers, switches and custom silicon to keep the system moving. • Power | $CEG, $VST, $GEV, $FPS, $VRT, $NVTS, $TLN, $ON Power determines whether new AI data centers can actually come online because the binding constraint is shifting from getting chips to getting megawatts so the value flows to the companies that control generation, grid equipment, power delivery, thermal management and efficiency. • Compute | $NBIS, $CIFR, $IREN, $APLD, $WULF, $CORZ, $CRWV Compute capacity is overflow layer when hyperscalers are sold out. Capital alone doesn't guarantee GPU access which is why buyers are signing multi-year contracts for clusters before they are even fully built. • CPU | $NVDA, $AMD, $INTC, $ARM, $QCOM On-device CPU (edge compute) becomes next bottleneck as AI moves into inference, agents, PCs, phones, vehicles and physical devices.

THE 5 BIGGEST BOTTLENECKS POWERING THE AI ECONOMY The way I'm thinking about AI winners today is that the market is moving beyond the simple question of which mega-cap company spends most on AI and has been rewarding the companies that control the scarce inputs, contracted capacity, data movement, power infrastructure, edge compute and workflow layers that make the AI economy function. These are the five bottlenecks I'm watching most: • Memory | $MU, Samsung, SK Hynix Memory is the clearest scarce input because HBM feeds the accelerator, only a few companies can make it at volume and buyers are locking in supply through long-term agreements that create revenue visibility through the end of the decade. • Connectivity | $AVGO, $MRVL, $ALAB, $CRDO, $AAOI, $ANET Connectivity determines whether AI clusters can move data fast enough because training runs span tens of thousands of chips that need to act like one machine. Once copper runs out of reach that causes the cluster depends on optics, retimers, switches and custom silicon to keep the system moving. • Power | $CEG, $VST, $GEV, $FPS, $VRT, $NVTS, $TLN, $ON Power determines whether new AI data centers can actually come online because the binding constraint is shifting from getting chips to getting megawatts so the value flows to the companies that control generation, grid equipment, power delivery, thermal management and efficiency. • Compute | $NBIS, $CIFR, $IREN, $APLD, $WULF, $CORZ, $CRWV Compute capacity is overflow layer when hyperscalers are sold out. Capital alone doesn't guarantee GPU access which is why buyers are signing multi-year contracts for clusters before they are even fully built. • CPU | $NVDA, $AMD, $INTC, $ARM, $QCOM On-device CPU (edge compute) becomes next bottleneck as AI moves into inference, agents, PCs, phones, vehicles and physical devices.

Shay Boloor

108,564 Aufrufe • vor 1 Monat

Jensen Huang told a room of global investors that AI is not one industry. It is five stacked on top of each other. Most people are investing in layer four and ignoring layers one through three entirely. He called it the five-layer cake. Layer one is energy. Jensen said this is the single greatest opportunity for the energy industry in a hundred years. The first time in a century that the grid in most countries can actually attract serious capital. Nuclear, solar, wind, hydrogen, it does not matter what form. If it produces energy, it gets funded. Siemens, GE Vernova, Mitsubishi. That is why they are all doing so well right now. Layer two is chips, computers, networking, and silicon photonics. Everything that processes the intelligence. Layer three is infrastructure. Land, power, buildings, data center operations. Every single one in short supply today. Layer four is the model layer. OpenAI, Anthropic. The layer everyone talks about. Layer five is applications. Every startup applying AI to financial services, legal, healthcare, logistics, transportation. Last year alone, a hundred billion dollars of venture capital went into this layer. The single largest VC year in the history of humanity. Then he said the number that stopped me cold. We are putting one trillion dollars into this five-layer cake this year. That sounds enormous. Jensen thinks the AI industry will eventually run at twenty trillion dollars per year. We are one trillion in of a twenty trillion dollar per year ecosystem. Most people watching AI are staring at layer four. Jensen was describing layers one through five as a single compounding system where every layer feeds the one above it. The people who understand that will invest differently than the people who do not.

Jensen Huang told a room of global investors that AI is not one industry. It is five stacked on top of each other. Most people are investing in layer four and ignoring layers one through three entirely. He called it the five-layer cake. Layer one is energy. Jensen said this is the single greatest opportunity for the energy industry in a hundred years. The first time in a century that the grid in most countries can actually attract serious capital. Nuclear, solar, wind, hydrogen, it does not matter what form. If it produces energy, it gets funded. Siemens, GE Vernova, Mitsubishi. That is why they are all doing so well right now. Layer two is chips, computers, networking, and silicon photonics. Everything that processes the intelligence. Layer three is infrastructure. Land, power, buildings, data center operations. Every single one in short supply today. Layer four is the model layer. OpenAI, Anthropic. The layer everyone talks about. Layer five is applications. Every startup applying AI to financial services, legal, healthcare, logistics, transportation. Last year alone, a hundred billion dollars of venture capital went into this layer. The single largest VC year in the history of humanity. Then he said the number that stopped me cold. We are putting one trillion dollars into this five-layer cake this year. That sounds enormous. Jensen thinks the AI industry will eventually run at twenty trillion dollars per year. We are one trillion in of a twenty trillion dollar per year ecosystem. Most people watching AI are staring at layer four. Jensen was describing layers one through five as a single compounding system where every layer feeds the one above it. The people who understand that will invest differently than the people who do not.

Ihtesham Ali

97,887 Aufrufe • vor 1 Monat

'Sinners' was one of 2025’s most special films, with magic that lived both on screen and behind the camera. At the #GoldenGlobes, we spoke with Miles Caton as he shared how mentorship and lessons from his co-star, Michael B. Jordan, guided him throughout the journey. Now imagine this being your first film and one of the leading-nominated movies of the night. C’mon, Miles!

'Sinners' was one of 2025’s most special films, with magic that lived both on screen and behind the camera. At the #GoldenGlobes, we spoke with Miles Caton as he shared how mentorship and lessons from his co-star, Michael B. Jordan, guided him throughout the journey. Now imagine this being your first film and one of the leading-nominated movies of the night. C’mon, Miles!

ESSENCE

16,108 Aufrufe • vor 6 Monaten

The market is underappreciating that $MSFT already owns the enterprise workflow layer where AI adoption is likely to happen. Workers already live inside Outlook, Excel, PowerPoint, Teams, GitHub and Azure which gives Microsoft a built-in distribution advantage as AI gets embedded across the enterprise. Copilot is still early but the numbers are getting more meaningful with over 20M paid seats, paid seat additions up 250% YoY and customers with more than 50K seats quadrupling YoY Every 10M Copilot seats at ~$30 per user per month represents ~$3.6B of annualized revenue so if Copilot scales to 100M seats over the next several years then it becomes a major software revenue layer on top of Microsoft 365.

The market is underappreciating that $MSFT already owns the enterprise workflow layer where AI adoption is likely to happen. Workers already live inside Outlook, Excel, PowerPoint, Teams, GitHub and Azure which gives Microsoft a built-in distribution advantage as AI gets embedded across the enterprise. Copilot is still early but the numbers are getting more meaningful with over 20M paid seats, paid seat additions up 250% YoY and customers with more than 50K seats quadrupling YoY Every 10M Copilot seats at ~$30 per user per month represents ~$3.6B of annualized revenue so if Copilot scales to 100M seats over the next several years then it becomes a major software revenue layer on top of Microsoft 365.

Shay Boloor

117,810 Aufrufe • vor 1 Monat

🚨 The Mask Is Off: DSA, ANSWER, and The People’s Forum Plan Weeks of “Escalation” At today’s ANSWER “Stop the War on Venezuela” webinar, Ashik Siddique, co-chair of DSA’s National Political Committee, pledged to “escalate” with The People’s Forum and ANSWER — “over the next few weeks.” Siddique is the DSA straightman, the buttoned-up milquetoast face of the organization, which is exactly why this matters. Even he is speaking in movement code about “the heart of empire,” “can’t leave any power on the table,” and finding “all the cracks” in a collapsing power structure and “hammer[ing] away.” This escalation plan is framed as a duty to weaken America from within. Don't be fooled by the "anti-war" framing. It’s an escalation pipeline packaged as solidarity.

🚨 The Mask Is Off: DSA, ANSWER, and The People’s Forum Plan Weeks of “Escalation” At today’s ANSWER “Stop the War on Venezuela” webinar, Ashik Siddique, co-chair of DSA’s National Political Committee, pledged to “escalate” with The People’s Forum and ANSWER — “over the next few weeks.” Siddique is the DSA straightman, the buttoned-up milquetoast face of the organization, which is exactly why this matters. Even he is speaking in movement code about “the heart of empire,” “can’t leave any power on the table,” and finding “all the cracks” in a collapsing power structure and “hammer[ing] away.” This escalation plan is framed as a duty to weaken America from within. Don't be fooled by the "anti-war" framing. It’s an escalation pipeline packaged as solidarity.

Stu Smith

79,040 Aufrufe • vor 7 Monaten

We’re excited to share a first look at the WandrLust demo, showing how real-world exploration becomes rewarding. 🌍 With WandrLust’s AI Agents guiding the journey, Natalie explores nearby places, tracks wellbeing, earns WANDR Points, and endorses moments that matter, all verified in the real world. As engagement grows, endorsements unlock $AFK, powering the social layer, rewards, and brand-backed experiences. This is an early iteration. We’ll keep refining as we build toward launch, but we hope this brings the WandrLust vision to life. 🚶‍♀️⚡️

WandrLust🌿Adventure Pays

11,781 Aufrufe • vor 6 Monaten

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.

Ricardo

640,736 Aufrufe • vor 5 Monaten

Ilya Sutskever, the co-founder of OpenAI, on the one AI risk that grows with every jump in capability. "There are concerns around if an AGI ever becomes very, very powerful, which is possible. Maybe it will want to go rogue, being that it is an agent." "We are also doing a lot of research on addressing the technological side of things, so that the AI will never want to go rogue. And this is something which I am working on as well." The work he is describing has a name: the assurance layer. Proving a powerful, agentic system will not go rogue before you deploy it, not after. That layer does not build itself. It gets built by the labs, regulators, and insurers who agree on how to test and verify these systems. P.S. That is the entire agenda of the AI Assurance and Governance Summit. Frontier labs, regulated industries, insurers, and investors in one room at Stanford on October 1. Reserve a seat: - Ilya Sutskever (Ilya Sutskever), co-founder of OpenAI, at TED

Ilya Sutskever, the co-founder of OpenAI, on the one AI risk that grows with every jump in capability. "There are concerns around if an AGI ever becomes very, very powerful, which is possible. Maybe it will want to go rogue, being that it is an agent." "We are also doing a lot of research on addressing the technological side of things, so that the AI will never want to go rogue. And this is something which I am working on as well." The work he is describing has a name: the assurance layer. Proving a powerful, agentic system will not go rogue before you deploy it, not after. That layer does not build itself. It gets built by the labs, regulators, and insurers who agree on how to test and verify these systems. P.S. That is the entire agenda of the AI Assurance and Governance Summit. Frontier labs, regulated industries, insurers, and investors in one room at Stanford on October 1. Reserve a seat: - Ilya Sutskever (Ilya Sutskever), co-founder of OpenAI, at TED

Karl Mehta

22,453 Aufrufe • vor 1 Monat

ONE BUILDER WIRED 4 RASPBERRY PI MODULES INTO A €450 AI CLUSTER WITH 64GB OF RAM AND KILLED HIS $200/MONTH CHATGPT SUBSCRIPTION 00:37 he points at the terminal, "loaded, loaded, loaded - 4 nodes synced over gigabit, 64 gigabytes total, llama 3.1 partitioned across the cluster" he stacked 4 raspberry pi CM5 lite modules with 16 gigabytes each onto a sipeed nanocluster board, the whole rig runs off a single 65 watt power supply and a gigabit internal switch distributed llama partitions the model across the nodes with synchronized workloads handled over the internal fabric, no quantization needed, the cluster hits about 30 tokens per second on small models a heavy developer pays $200 a month for chatgpt pro and another $200 for claude code, this rig cost €450 in parts and breaks even on the stack in month 2 most people will keep paying anthropic and openai forever, a few will spend a weekend wiring 4 raspberry pi modules and never see a subscription invoice again the window is open, follow and bookmark before it closes

ONE BUILDER WIRED 4 RASPBERRY PI MODULES INTO A €450 AI CLUSTER WITH 64GB OF RAM AND KILLED HIS $200/MONTH CHATGPT SUBSCRIPTION 00:37 he points at the terminal, "loaded, loaded, loaded - 4 nodes synced over gigabit, 64 gigabytes total, llama 3.1 partitioned across the cluster" he stacked 4 raspberry pi CM5 lite modules with 16 gigabytes each onto a sipeed nanocluster board, the whole rig runs off a single 65 watt power supply and a gigabit internal switch distributed llama partitions the model across the nodes with synchronized workloads handled over the internal fabric, no quantization needed, the cluster hits about 30 tokens per second on small models a heavy developer pays $200 a month for chatgpt pro and another $200 for claude code, this rig cost €450 in parts and breaks even on the stack in month 2 most people will keep paying anthropic and openai forever, a few will spend a weekend wiring 4 raspberry pi modules and never see a subscription invoice again the window is open, follow and bookmark before it closes

Antid

64,077 Aufrufe • vor 1 Monat

Never trust someone who pretends to be a Hindu - Suriya is a classic example! Suriya Sivakumar I came across this video of Suriya and Jyothika visiting Tirupati. What makes this amusing is that Suriya maintained complete silence when Jyothika questioned why people donate money to temples instead of hospitals. Not a word. Not even a clarification. But now suddenly, temple visits and pilgrimage optics? If temple donations are supposedly a problem, then why the need for publicized Tirupati trips and spiritual branding now? This is exactly why many people see cinema celebrities as deeply opportunistic. Religion for them often appears less like faith and more like image management depending on the political climate and public mood. Suriya has spent years carefully aligning himself with the Dravidian ecosystem while conveniently balancing religious optics whenever needed. That contradiction is becoming increasingly obvious to people. I also hear *Karuppu* is struggling because audiences are slowly becoming tired of subtle propaganda packaged as commercial cinema. People can tolerate ideology. What they dislike is hypocrisy. Never trust someone who wears Hinduism only when it is convenient.

Never trust someone who pretends to be a Hindu - Suriya is a classic example! Suriya Sivakumar I came across this video of Suriya and Jyothika visiting Tirupati. What makes this amusing is that Suriya maintained complete silence when Jyothika questioned why people donate money to temples instead of hospitals. Not a word. Not even a clarification. But now suddenly, temple visits and pilgrimage optics? If temple donations are supposedly a problem, then why the need for publicized Tirupati trips and spiritual branding now? This is exactly why many people see cinema celebrities as deeply opportunistic. Religion for them often appears less like faith and more like image management depending on the political climate and public mood. Suriya has spent years carefully aligning himself with the Dravidian ecosystem while conveniently balancing religious optics whenever needed. That contradiction is becoming increasingly obvious to people. I also hear Karuppu is struggling because audiences are slowly becoming tired of subtle propaganda packaged as commercial cinema. People can tolerate ideology. What they dislike is hypocrisy. Never trust someone who wears Hinduism only when it is convenient.

Praveen

385,167 Aufrufe • vor 2 Monaten