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🎙️ Want a full breakdown on why cost per token is a key metric for AI infrastructure TCO? We’re operating in a power-constrained world. Demand for intelligence is scaling faster than infrastructure can keep up—so metrics like cost per GPU hour or FLOPs per dollar don’t tell the full...

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NVIDIA

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21,970 просмотров • 4 месяцев назад •via X (Twitter)

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“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️
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“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️

NVIDIA

88,975 просмотров • 4 месяцев назад

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓
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Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓

Сarm1ne

128,114 просмотров • 24 дней назад

$NVDA has the “lowest cost per token in the world” which is really a claim about owning the economics of inference. The company that delivers the most tokens per watt, per rack and per dollar is the one that captures the most demand and the best margins as AI scales.
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$NVDA has the “lowest cost per token in the world” which is really a claim about owning the economics of inference. The company that delivers the most tokens per watt, per rack and per dollar is the one that captures the most demand and the best margins as AI scales.

Shay Boloor

48,594 просмотров • 4 месяцев назад

The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.
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The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.

Six Five Media

294,803 просмотров • 4 месяцев назад

📈 NVIDIA tops AI leaderboards and benchmarks with open models driven by extreme co-design across compute, networking, memory, storage, and software. This includes models for biology, AI physics, agentic AI, physical AI, robotics, and autonomous vehicles. By being vertically integrated and horizontally open, NVIDIA enables its ecosystem to scale agentic workflows at every layer of the stack with the lowest cost per token.
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📈 NVIDIA tops AI leaderboards and benchmarks with open models driven by extreme co-design across compute, networking, memory, storage, and software. This includes models for biology, AI physics, agentic AI, physical AI, robotics, and autonomous vehicles. By being vertically integrated and horizontally open, NVIDIA enables its ecosystem to scale agentic workflows at every layer of the stack with the lowest cost per token.

NVIDIA

85,569 просмотров • 3 месяцев назад

"The inflection point for inference has arrived." — Jensen Huang, Founder & CEO of NVIDIA We’ve officially crossed a new milestone in the inference era — where widespread adoption of AI shifts from learning to doing. The breakthrough: extreme codesign across hardware and software driving down cost per token. Lower cost → more inference More inference → more users and applications More users and applications → exponential AI revenue growth
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"The inflection point for inference has arrived." — Jensen Huang, Founder & CEO of NVIDIA We’ve officially crossed a new milestone in the inference era — where widespread adoption of AI shifts from learning to doing. The breakthrough: extreme codesign across hardware and software driving down cost per token. Lower cost → more inference More inference → more users and applications More users and applications → exponential AI revenue growth

NVIDIA

81,834 просмотров • 4 месяцев назад

Delivered performance, not peak chip specifications, drives AI factory productivity. Rigorous benchmarks are the only way to see past the noise. In MLPerf Inference v6.0, NVIDIA extreme co-design delivered the highest token output across the broadest range of models and scenarios. Maximizing token output drives down token cost and maximizes AI factory productivity. Read the blog post to dive into the details: Baseten, CoreWeave, MLCommons
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Delivered performance, not peak chip specifications, drives AI factory productivity. Rigorous benchmarks are the only way to see past the noise. In MLPerf Inference v6.0, NVIDIA extreme co-design delivered the highest token output across the broadest range of models and scenarios. Maximizing token output drives down token cost and maximizes AI factory productivity. Read the blog post to dive into the details: Baseten, CoreWeave, MLCommons

NVIDIA

27,483 просмотров • 4 месяцев назад

DON’T SELL YOUR XRP. Crypto is preparing for another major run, and #XRP could potentially break above $4 before the end of August. 🚀 REAL TOKEN — built on the XRP Ledger and powering the RealFi ecosystem — is positioning itself as one of the leading DeFi tokens within the XRPL ecosystem as adoption continues expanding globally. 🌎⚡ WE’RE TALKING ABOUT A TOKEN TARGETING MULTI-TRILLION-DOLLAR INDUSTRIES THROUGH XRPL-POWERED PAYMENT AND DIGITAL INFRASTRUCTURE. 🚀 JUST STACKED EVEN MORE REAL TOKEN 👀🔥 BUY REAL TOKEN ON DEX: BUY REAL TOKEN ON MEXC: 🏆‼️ IF REAL TOKEN WERE TO REACH A $100 BILLION MARKET CAP, IT COULD POTENTIALLY RISE FROM $0.02 TO APPROXIMATELY $748.50 PER TOKEN.
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DON’T SELL YOUR XRP. Crypto is preparing for another major run, and #XRP could potentially break above $4 before the end of August. 🚀 REAL TOKEN — built on the XRP Ledger and powering the RealFi ecosystem — is positioning itself as one of the leading DeFi tokens within the XRPL ecosystem as adoption continues expanding globally. 🌎⚡ WE’RE TALKING ABOUT A TOKEN TARGETING MULTI-TRILLION-DOLLAR INDUSTRIES THROUGH XRPL-POWERED PAYMENT AND DIGITAL INFRASTRUCTURE. 🚀 JUST STACKED EVEN MORE REAL TOKEN 👀🔥 BUY REAL TOKEN ON DEX: BUY REAL TOKEN ON MEXC: 🏆‼️ IF REAL TOKEN WERE TO REACH A $100 BILLION MARKET CAP, IT COULD POTENTIALLY RISE FROM $0.02 TO APPROXIMATELY $748.50 PER TOKEN.

KingXRP

29,645 просмотров • 3 месяцев назад

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026
0:49

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026

Mike

145,992 просмотров • 2 месяцев назад

Erik Voorhees on Venice and the convergence thesis: "Venice is a private, uncensored version of ChatGPT with crypto ethos baked in." "DIEM token holders get a dollar per day of free compute, zero marginal cost inference for agents." "Cryptocurrency was actually just being built for the AI robots. Humans were always awkward with it. Agents won't be."
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Erik Voorhees on Venice and the convergence thesis: "Venice is a private, uncensored version of ChatGPT with crypto ethos baked in." "DIEM token holders get a dollar per day of free compute, zero marginal cost inference for agents." "Cryptocurrency was actually just being built for the AI robots. Humans were always awkward with it. Agents won't be."

The Rollup

47,917 просмотров • 3 месяцев назад

Every AI product running in production depends on an inference system someone had to engineer, optimize and more. Scheduling, batching, routing, cost per token. This is a craft. The Inference Frontier Program spotlights the builders behind that work. 💡 Watch the video and nominate a team:
1:22

Every AI product running in production depends on an inference system someone had to engineer, optimize and more. Scheduling, batching, routing, cost per token. This is a craft. The Inference Frontier Program spotlights the builders behind that work. 💡 Watch the video and nominate a team:

Nebius

54,224 просмотров • 5 месяцев назад

The City of Los Angeles is inflating the cost to replace curb ramps on street corners Cost for curb ramps per corner: Beverly Hills - $10,000 Caltrans - $15,000 City of LA - $50,000+ - In 2014 the cost was only $3,000 per ramp - In the early 2000s it only cost about $1,800 per ramp on average But now Democrats have ballooned this cost to over $50,000 You can’t tell me this is anything other than money laundering and deals with outside companies with kickbacks involved California is run by a lawless mafia
2:58

The City of Los Angeles is inflating the cost to replace curb ramps on street corners Cost for curb ramps per corner: Beverly Hills - $10,000 Caltrans - $15,000 City of LA - $50,000+ - In 2014 the cost was only $3,000 per ramp - In the early 2000s it only cost about $1,800 per ramp on average But now Democrats have ballooned this cost to over $50,000 You can’t tell me this is anything other than money laundering and deals with outside companies with kickbacks involved California is run by a lawless mafia

Wall Street Apes

117,865 просмотров • 2 месяцев назад

ELON MUSK: "The AI 5 chip which is going to be a great chip. All of Tesla hinges on that chip. That's the chip that going to our next gen self driving cars and it's also essential for Optimus. It's performance per watt is extremely good. It is probably going to be at least 2-3 times better than NVIDIA performance per watt and 10% cost of the NVIDIA chip something like that."
1:52

ELON MUSK: "The AI 5 chip which is going to be a great chip. All of Tesla hinges on that chip. That's the chip that going to our next gen self driving cars and it's also essential for Optimus. It's performance per watt is extremely good. It is probably going to be at least 2-3 times better than NVIDIA performance per watt and 10% cost of the NVIDIA chip something like that."

DogeDesigner

162,829 просмотров • 8 месяцев назад

Scaling performance is only part of the equation. The other half is cost. And what we are seeing right now is an unprecedented collapse in cost at industrial scale. Three years ago, running a million-token output with AI cost about sixty dollars. Today, it costs less than a cent. That is a 99.9 percent drop. In any other sector, that would be called a revolution. When something this powerful becomes that cheap, it is no longer a specialist tool. It becomes a foundational utility. This is the shift from niche capability to industrial infrastructure.
1:00

Scaling performance is only part of the equation. The other half is cost. And what we are seeing right now is an unprecedented collapse in cost at industrial scale. Three years ago, running a million-token output with AI cost about sixty dollars. Today, it costs less than a cent. That is a 99.9 percent drop. In any other sector, that would be called a revolution. When something this powerful becomes that cheap, it is no longer a specialist tool. It becomes a foundational utility. This is the shift from niche capability to industrial infrastructure.

Nina Schick

30,435 просмотров • 8 месяцев назад

Live on Cline (v3.67.1) OpenAI 's GPT 5.3 Codex. The speed and token efficiency improvements are real. Here is what's new: > 25% faster than 5.2 Codex > #1 on SWE-Bench Pro (4 different programming languages) > Fewer tokens per task than any prior OpenAI model Runs cost less and finish faster. You can use it in Cline right now without setting up an API key. Select the model and try it on your repo.
0:31

Live on Cline (v3.67.1) OpenAI 's GPT 5.3 Codex. The speed and token efficiency improvements are real. Here is what's new: > 25% faster than 5.2 Codex > #1 on SWE-Bench Pro (4 different programming languages) > Fewer tokens per task than any prior OpenAI model Runs cost less and finish faster. You can use it in Cline right now without setting up an API key. Select the model and try it on your repo.

Cline

21,291 просмотров • 5 месяцев назад

🚨The CFTC, a key US government agency, has revealed a strategic alliance with Ripple, involving the utilization of #XRP! This partnership signals the future integration of XRP across the entire financial ecosystem! The highly anticipated CTF token, built on the XRPL, is poised for tremendous growth as the demand for its infrastructure soars! With just a $20 billion market cap, the CTF token could skyrocket from $0.70 to an incredible $748.50 per token! Get ready for explosive gains! CTF token is a hidden gem, and its limited supply makes it even more attractive! Trade CTF Token here: Trade CTF Token on MEXC: For more information:
1:48

🚨The CFTC, a key US government agency, has revealed a strategic alliance with Ripple, involving the utilization of #XRP! This partnership signals the future integration of XRP across the entire financial ecosystem! The highly anticipated CTF token, built on the XRPL, is poised for tremendous growth as the demand for its infrastructure soars! With just a $20 billion market cap, the CTF token could skyrocket from $0.70 to an incredible $748.50 per token! Get ready for explosive gains! CTF token is a hidden gem, and its limited supply makes it even more attractive! Trade CTF Token here: Trade CTF Token on MEXC: For more information:

JackTheRippler ©️

46,811 просмотров • 1 год назад

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."
6:17

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Molly O’Shea

12,816 просмотров • 1 месяц назад

🚨 WHEN #XRP REPLACES SWIFT, COULD IT SURPASS $15,000 PER COIN 🚨 Some analysts believe that if XRP replaces SWIFT and captures a significant share of global payment flows, its value could potentially exceed $15,000 per coin. And it's not just hype—the argument is that XRP was specifically built for global payments at scale. The XRP Ledger narrative continues to expand into a multi-trillion-dollar opportunity, with REAL Token emerging as a DeFi utility asset focused on driving liquidity, activity, and growth across the XRPL ecosystem. ⚡ 👀 Early positioning in emerging ecosystems like this can be a key differentiator. BUY REAL TOKEN ON DEX: BUY REAL TOKEN ON MEXC: 🚀 In an ultra-bullish $100B market cap scenario, some models suggest REAL Token could potentially increase from $0.02 to approximately $748.50.
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🚨 WHEN #XRP REPLACES SWIFT, COULD IT SURPASS $15,000 PER COIN 🚨 Some analysts believe that if XRP replaces SWIFT and captures a significant share of global payment flows, its value could potentially exceed $15,000 per coin. And it's not just hype—the argument is that XRP was specifically built for global payments at scale. The XRP Ledger narrative continues to expand into a multi-trillion-dollar opportunity, with REAL Token emerging as a DeFi utility asset focused on driving liquidity, activity, and growth across the XRPL ecosystem. ⚡ 👀 Early positioning in emerging ecosystems like this can be a key differentiator. BUY REAL TOKEN ON DEX: BUY REAL TOKEN ON MEXC: 🚀 In an ultra-bullish $100B market cap scenario, some models suggest REAL Token could potentially increase from $0.02 to approximately $748.50.

XRP QUEEN🤍

18,634 просмотров • 2 месяцев назад

ELON MUSK: "We see a path to putting 100 gigawatts per year of solar-powered AI satellite into orbit. And having this be actually the lowest cost way to power and operate AI at a very large scale For reference, the United States consumes roughly 460 gigawatts on average per year. Because the average power load in the US is 460 gigawatts"
0:51

ELON MUSK: "We see a path to putting 100 gigawatts per year of solar-powered AI satellite into orbit. And having this be actually the lowest cost way to power and operate AI at a very large scale For reference, the United States consumes roughly 460 gigawatts on average per year. Because the average power load in the US is 460 gigawatts"

X Freeze

9,044,739 просмотров • 9 месяцев назад