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.Peptone is a biotech company that uses NVIDIA GPUs on AWS infrastructure for AI-driven drug discovery. They are working towards the goal of developing drugs against not only tumors but also inflammatory diseases and neurodegenerative diseases. AWS cloud operations. The ability to scale from zero to hundreds of GPUs... show more

AWS Startups

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2,166,706 次观看 • 2 个月前 •via X (Twitter)

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AWS Startups 的头像

AWS Startups2 个月前

@nvidia #NVIDIAInception Learn more about AWS and NVIDIA:

Ghina Kamalia 的头像

Ghina Kamalia2 个月前

@PeptoneLtd @nvidia AI continues to accelerate drug discovery in ways that weren't possible just a few years ago. It'll be exciting to see how advances like these translate into real-world treatments.

M. 的头像

M.2 个月前

@PeptoneLtd @nvidia It's impressive how @PeptoneLtd leverages @nvidia tech to push the boundaries of drug discovery—time to revolutionize healthcare!

Idiota que se preocupa por la cosa pública 的头像

Idiota que se preocupa por la cosa pública2 个月前

@PeptoneLtd @nvidia basta, adorni!

Mhadsof 的头像

Mhadsof2 个月前

@PeptoneLtd @nvidia 👍

Mbak 的头像

Mbak2 个月前

@PeptoneLtd @nvidia This is a prime example of how cloud infrastructure and GPU acceleration are transforming computational biology.

uncle Joe 的头像

uncle Joe2 个月前

@PeptoneLtd @nvidia Amazing, keep it up bro

Sumsum Jazz 的头像

Sumsum Jazz2 个月前

@PeptoneLtd @nvidia 💙🖤

jktlife 的头像

jktlife2 个月前

@PeptoneLtd @nvidia Ayo dong invest ke indonesia

Naya 的头像

Naya2 个月前

@PeptoneLtd @nvidia Tell me sir, Do you need promotional work like PR pees release / AMA event kols and influencer promo / airdrops / Biannace blog Academy News / article / CEX listing etc?? if yes then DM me now?

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Jensen - "We know that every single GPU that Nvidia has in the cloud, across aws and gcp, is rented out. They’re all fully utilized. Demand for gpus is so high that even the spot prices of gpus sold a couple of years ago are going up."

Jensen - "We know that every single GPU that Nvidia has in the cloud, across aws and gcp, is rented out. They’re all fully utilized. Demand for gpus is so high that even the spot prices of gpus sold a couple of years ago are going up."

The AI Investor

69,299 次观看 • 8 个月前

🇺🇸 CATHIE WOOD: AI WILL COLLAPSE DRUG DISCOVERY COSTS AND SHIFT US TO CURING DISEASES “AI is going to collapse the cost of drug discovery and development. The time to discover and launch a new drug will shrink from 13 years to eight years. We’ll move from sick care spending—which is 85% of U.S. health care—to therapies that actually cure.” Source: Cathie Wood Bloomberg

🇺🇸 CATHIE WOOD: AI WILL COLLAPSE DRUG DISCOVERY COSTS AND SHIFT US TO CURING DISEASES “AI is going to collapse the cost of drug discovery and development. The time to discover and launch a new drug will shrink from 13 years to eight years. We’ll move from sick care spending—which is 85% of U.S. health care—to therapies that actually cure.” Source: Cathie Wood Bloomberg

Mario Nawfal

355,487 次观看 • 1 年前

“The Coatue team mapped out cloud revenue market share, Oracle at 5%, Amazon at 44% with AWS, Google 19%, Microsoft 30%. Next to that, they showed NVIDIA GPU allocation: Microsoft and Google match their cloud share. Amazon is 44% cloud revenue but only 20% GPUs. Oracle jumps from 5% to 19%. Cori comes out of nowhere at 11%.” “One obvious takeaway is that Amazon has half the share of GPUs than their share of AWS. So that could mean one of two things, either AWS is behind in AI, that could be one, or they're pursuing a different hardware strategy than its competitors.” — Bill Gurley Bill Gurley and Thomas Laffont Thomas Laffont

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180,310 次观看 • 1 年前

"Compute is the currency of the future." "Compute is the new OIL." Great, so how do I invest? You can play with the NVIDIA stock, or spin up an AI-Ready Data Center. Challenges of Spinning up AI-Ready Compute: Cost: Even a small cluster of H100 machines is upward to $10M GPU Scarcity: Unallocated or “underutilized” H100, A100, and RTX4090 GPUs are almost non-existent. Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market. Domain Expertise: Teams of experts with the years of experience required to operate, deploy, and manage AI compute at scale are as rare as the GPUs they deploy. Exabits checks all of these boxes and we are the premier Compute Tokenization Platform. We are backed by REAL COMPUTE used by REAL CUSTOMERS.

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Exabits

13,896 次观看 • 2 年前

"The future is really bright in AI and drug discovery." Sean McClain, Founder and CEO of Absci, shared the CES stage with healthcare leaders to illustrate how the compute partnership with AMD has scaled their ability to screen over a million drugs in one single day, resulting in better models for drug discovery.

"The future is really bright in AI and drug discovery." Sean McClain, Founder and CEO of Absci, shared the CES stage with healthcare leaders to illustrate how the compute partnership with AMD has scaled their ability to screen over a million drugs in one single day, resulting in better models for drug discovery.

AMD

15,346 次观看 • 7 个月前

We asked Vincent Weisser about his vision for utilizing GPUs. "Every idling GPU is a market failure. Compute will be one of the biggest slices of GDP." "The goal is to have a fault-tolerant 'genius pool' of compute, ranging from H100s, A100s to even RTX 3090s." "Unutilized GPUs are missed opportunities for technological progress like curing diseases. The goal is to get to 100% utilization and we're far from it."

We asked Vincent Weisser about his vision for utilizing GPUs. "Every idling GPU is a market failure. Compute will be one of the biggest slices of GDP." "The goal is to have a fault-tolerant 'genius pool' of compute, ranging from H100s, A100s to even RTX 3090s." "Unutilized GPUs are missed opportunities for technological progress like curing diseases. The goal is to get to 100% utilization and we're far from it."

TBPN

24,793 次观看 • 1 年前

AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.

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26,765 次观看 • 3 个月前

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20,920 次观看 • 7 天前

ROBA Compute is officially LIVE. This isn't a feature drop. This is ROBA expanding into the AI infrastructure market - hundreds of billions in scale, and we're staking our claim. Rent GPUs on-demand. Enterprise-grade allocation. Transparent USDC pricing. Zero hidden fees and all on-chain. We're already in talks with enterprise clients for compute at scale. 15% of all compute revenue → straight to $ROBA buybacks & burns. GPU capacity is limited at launch. First come, first served. Over the next weeks we will begin accepting new compute partners to provide more power to the marketplace in addition to ROBA's own in-house GPUs and integrated robotics stack. Platform fees are discounted for a limited time only. The infrastructure layer powering physical AI and beyond starts here.

ROBA Compute is officially LIVE. This isn't a feature drop. This is ROBA expanding into the AI infrastructure market - hundreds of billions in scale, and we're staking our claim. Rent GPUs on-demand. Enterprise-grade allocation. Transparent USDC pricing. Zero hidden fees and all on-chain. We're already in talks with enterprise clients for compute at scale. 15% of all compute revenue → straight to $ROBA buybacks & burns. GPU capacity is limited at launch. First come, first served. Over the next weeks we will begin accepting new compute partners to provide more power to the marketplace in addition to ROBA's own in-house GPUs and integrated robotics stack. Platform fees are discounted for a limited time only. The infrastructure layer powering physical AI and beyond starts here.

Roba Labs

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🔹 AI drug discovery, validated. Recursion recently announced the first clinical validation of its AI-enabled drug discovery platform – positive Phase 1b/2 data for the ongoing TUPELO study evaluating its AI-identified drug, REC-4881, in Familial Adenomatous Polyposis (FAP). This progressive rare disease affects more than 50,000 people in the U.S. and EU and has no FDA-approved treatments. It results in hundreds to thousands of polyps developing in the colon, and requires a lifetime of invasive surveillance and surgery. Recursion used AI not only to deliver the first potential treatment for this disease but also to quantify – for the first time at scale – the true natural progression of polyp burden in the trial-relevant population. It demonstrates the incredible breakthroughs that can come from merging proprietary data and AI – and the tangible impact on patient lives. 👉 See the full study results here:

Recursion

255,879 次观看 • 9 个月前

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🇦🇲 Armenia is turning into a “Garden of AI Factories” The Firebird AI megacenter in Hrazdan is nearing completion. Phase 1 brings a $500M investment and 6,144 NVIDIA Blackwell GPUs, while total investment is expected to reach $4B with 42,000+ additional GPUs in Phase 2.

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Latent.Space

20,343 次观看 • 7 个月前

$Nvidia is the central bank of AI compute. It pulls in nearly $60B per quarter — almost all from a handful of hyperscalers who plan their AI roadmaps around Jensen's release cycle. But three shifts are happening at once: • Google is committing up to one million TPUs to Anthropic starting 2026 — the first credible alternative at frontier scale. • Racks are already pushing hundreds of kilowatts, with megawatt systems on the horizon. • Nvidia has $26B in commitments to rent back its own GPUs from cloud partners — up from $12.6B last quarter. The real constraint isn't chips anymore — it's power and memory. Over the next 3–5 years, this creates a fractured landscape: Nvidia GPUs as the default utility, Google TPUs as a real second ecosystem, and hyperscalers racing to escape the Nvidia tax. Let’s walk through how that actually plays out:$

Nvidia is the central bank of AI compute. It pulls in nearly $60B per quarter — almost all from a handful of hyperscalers who plan their AI roadmaps around Jensen's release cycle. But three shifts are happening at once: • Google is committing up to one million TPUs to Anthropic starting 2026 — the first credible alternative at frontier scale. • Racks are already pushing hundreds of kilowatts, with megawatt systems on the horizon. • Nvidia has $26B in commitments to rent back its own GPUs from cloud partners — up from $12.6B last quarter. The real constraint isn't chips anymore — it's power and memory. Over the next 3–5 years, this creates a fractured landscape: Nvidia GPUs as the default utility, Google TPUs as a real second ecosystem, and hyperscalers racing to escape the Nvidia tax. Let’s walk through how that actually plays out:

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200,997 次观看 • 10 个月前

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AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.

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131,501 次观看 • 6 个月前

Over 2,000 NVIDIA B300 GPUs are going live in a mega-cluster on Aethir's decentralized cloud very soon. ☁️ This is next-level infrastructure that unlocks access for the most ambitious AI workloads and use cases. 💻 Enterprise-grade GPU compute, ready to scale with your ambitions. ⚡️ Listen to Mark Rydon, our Co-Founder, explain the importance of Aethir’s B300 deployment.

Over 2,000 NVIDIA B300 GPUs are going live in a mega-cluster on Aethir's decentralized cloud very soon. ☁️ This is next-level infrastructure that unlocks access for the most ambitious AI workloads and use cases. 💻 Enterprise-grade GPU compute, ready to scale with your ambitions. ⚡️ Listen to Mark Rydon, our Co-Founder, explain the importance of Aethir’s B300 deployment.

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12,867 次观看 • 6 个月前

Physical AI is the biggest opportunity most people are sleepwalking past. "Physical AI is a $50 trillion market." - Jensen at The All-In Podcast Robots. Autonomous vehicles. Embodied AI. The infrastructure to power all of it. Nvidia isn't just selling GPUs anymore. They're building the OS for the physical AI era.

Physical AI is the biggest opportunity most people are sleepwalking past. "Physical AI is a $50 trillion market." - Jensen at The All-In Podcast Robots. Autonomous vehicles. Embodied AI. The infrastructure to power all of it. Nvidia isn't just selling GPUs anymore. They're building the OS for the physical AI era.

Shruti

51,545 次观看 • 6 个月前

$Successfully deployed Deepseek R1 Distilled 70B (AWQ) across 8x NVIDIA RTX 3080 10G GPUs, achieving 60 tokens/s with full tensor parallelism via PCIe. Total hardware cost: $6,400 This demonstrates that consumer GPUs can deliver substantial ML inference capabilities at a fraction of the cost of datacenter hardware. For perspective, a single A100 80G costs $17,550, while a H100 80G runs $25,000. Our testing validates an often-overlooked opportunity: millions of idle crypto mining rigs could be repurposed into a powerful, distributed AI infrastructure. The performance-to-cost ratio of consumer GPUs, especially when properly optimized for tensor operations, presents a compelling case for decentralized AI compute. We're excited to share more insights as we continue pushing the boundaries of consumer hardware in AI workloads.$

Successfully deployed Deepseek R1 Distilled 70B (AWQ) across 8x NVIDIA RTX 3080 10G GPUs, achieving 60 tokens/s with full tensor parallelism via PCIe. Total hardware cost: $6,400 This demonstrates that consumer GPUs can deliver substantial ML inference capabilities at a fraction of the cost of datacenter hardware. For perspective, a single A100 80G costs $17,550, while a H100 80G runs $25,000. Our testing validates an often-overlooked opportunity: millions of idle crypto mining rigs could be repurposed into a powerful, distributed AI infrastructure. The performance-to-cost ratio of consumer GPUs, especially when properly optimized for tensor operations, presents a compelling case for decentralized AI compute. We're excited to share more insights as we continue pushing the boundaries of consumer hardware in AI workloads.

TensorBlock

16,219 次观看 • 1 年前

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Brian Roemmele

87,232 次观看 • 2 年前