7 ms·

What do you think labs are doing with the minimum 10TB memory in NvLink 72 systems that were publicly reported to all start coming online in November/December o

by Chamix 7mo ago

What do you think labs are doing with the minimum 10TB memory in NvLink 72 systems that were publicly reported to all start coming online in November/December of last year? And why would this 1 TB -> 10 TB jump matter so much for Anthropic previously being wholly dependent on running Opus 4x on TPUs, if the models were 2-3T at 4bit and could fit in 8x B200 (1.5 TB = 3T param) widely deployed during the Opus 4 era? You have presented a vibe-based rebuttal with no evidence or or logic to outline why you think labs are still stuck in the single trillions of parameters (GPT 4 was ~1 trillion params!). Though, you have successfully cunninghammed me into saying that while anything I publicly state is derived from public info, working in the industry itself is a helpful guide to point at the right public info to reference.

johndough 7mo ago
Could you point at some more public info about active parameter count? You said: > and while an exact number is hard to compute, let me tell you, it is not 17B or anywhere in that particular OOM :) I can see ~100B, but that would near the same order of magnitude. I find ~1000B active parameters hard to believe.
- Chamix 7mo ago
  Sorry if that was unclear, I did mean 100Bs as in the next order of magnitude. Even GPT-4 had ~220B active params, though the trend has been towards increased sparsification (lower activation:total ratio). GPT 4.5 is the only publicly facing model that approached 1T active parameters (an experiment to see if there was any value in the extreme inference cost of quadratically increasing compute cost with naïve-like attention). Nowadays you optimize your head size to your attention kernel arch and obtain performance principally through inference time scaling (generate more of tokens) and parallel consensus (gpt pro, gemini deep think etc), both of which favor faster, cheaper active heads. 4o and other H100 era models did indeed drop their activated heads far smaller than gpt-4 to the 10s just like current Hopper-Era Chinese open-source, but it went right back up again post-Blackwell with the 10x L2 bump (for kv cache) in congruence with nlogn attention mechanisms being refined. Similar story for Claude. The fun speculation is wondering about the true size of Gemini 3's internals, given the petabyte+ world size of their homefield IronwoodV7 systems and Jim Keller's public penchant for envisioning extreme MoE-like diversification across hundreds of dedicated sub-models constructed by individual teams within DeepMind.
jychang 7mo ago
Well, for one, Anthropic mostly uses Google TPUs and Amazon Inferentia2 chips, not Nvidia NVL72s. That's because... Google and Amazon are major investors in Anthropic. Secondly, you missed out the entire AI industry trend in 2024-2025, where the failure of the GPT-4.5 pretrain run and the pullback from GPT-4 to GPT-4 Turbo to GPT-4o (each of which are smaller in parameter count). GPT-4 is 1.6T, GPT-4 Turbo is generally considered 1/2 to 1/4 that, and GPT-4o is even smaller (details below) Thirdly, we KNOW that GPT-4o runs on Microsoft Maia 100 hardware with 64GB each chip, which gives a hard limit on the size of GPT-4o and tells us that it's a much smaller distilled version of GPT-4. Microsoft says each server has 4 Maia 100 chips and 256GB total. We know Microsoft uses Maia 100s to serve GPT-4o for Azure! So we know that quantized GPT-4o fits in 256GB, and GPT-4 does not fit. It's not possible to have GPT-4o be some much larger model that requires a large cluster to serve- that would drop performance below what we see in Azure. Fourthly, it is not publicly KNOWN, but leaks say that GPT-4o is 200b-300b in size, which also tells us that running GPT-4 sized models is nonsense. This matches the information from Microsoft Maia servers above. Fifthly, OpenAI Head of Research has since confirmed that o1, o3, GPT-5 use the same pretrain run as 4o, so they would be the same size.[1] That means GPT-5 is not some 1T+ model! Semianalysis confirms that the only pretrain run since 4o is 4.5, which is a ~10T model but everyone knows is a failed run. Sixthly, Amazon Bedrock and Google Vertex serves models at approximately similar memory bandwidths when calculating tokens/sec, giving 4900GB/sec for Google Vertex. Opus 4.5 aligns very well with 100b of active params. 42 tps for Claude Opus 4.6 https://openrouter.ai/anthropic/claude-opus-4.6 143 tps for GLM 4.7 (32B active parameters) https://openrouter.ai/z-ai/glm-4.7 70 tps for Llama 3.3 70B (dense model) https://openrouter.ai/meta-llama/llama-3.3-70b-instruct For GLM 4.7, that makes 143 * 32B = 4576B parameters per second, and for Llama 3.3, we get 70 * 70B = 4900B. There's calculations for Amazon Bedrock on the Opus 4.5 launch thread that compares it to gpt-oss-120b with similar conclusions. Seventhly, Anthropic distilled Opus 4/4.1 to 4.5, which is why it runs ~3x faster than Opus 4 while costing 1/3 the price in terms of API fees. Eightly, no respectable model has a sparsity below 3% these days- ridiculously low sparsity gives you Llama 4. Every single cutting edge model are around 3-5% sparsity. Knowing the active param count for Opus 4.5 gives you a very good estimate of total param count. The entire AI industry is moving AWAY from multi-trillion-parameter models. Everything is about increasing efficiency with the amount of parameters you have, not hyperscaling like GPT-4.5 which was shown to be a bad way forward. Nobody thinks Opus 4.5 is bigger than around 2T in size (so not 10T). Opus 4/4.1 may have been ~6T, but that's it. Any guess of 10T or above is patently ridiculous for both Opus 4/4.1 and Opus 4.5. [1] https://x.com/petergostev/status/1995744289079656834 https://x.com/petergostev/status/1995744289079656834

by Chamix 7mo ago

johndough 7mo ago

Could you point at some more public info about active parameter count? You said: > and while an exact number is hard to compute, let me tell you, it is not 17B or anywhere in that particular OOM :) I can see ~100B, but that would near the same order of magnitude. I find ~1000B active parameters hard to believe.

Chamix 7mo ago
Sorry if that was unclear, I did mean 100Bs as in the next order of magnitude. Even GPT-4 had ~220B active params, though the trend has been towards increased sparsification (lower activation:total ratio). GPT 4.5 is the only publicly facing model that approached 1T active parameters (an experiment to see if there was any value in the extreme inference cost of quadratically increasing compute cost with naïve-like attention). Nowadays you optimize your head size to your attention kernel arch and obtain performance principally through inference time scaling (generate more of tokens) and parallel consensus (gpt pro, gemini deep think etc), both of which favor faster, cheaper active heads. 4o and other H100 era models did indeed drop their activated heads far smaller than gpt-4 to the 10s just like current Hopper-Era Chinese open-source, but it went right back up again post-Blackwell with the 10x L2 bump (for kv cache) in congruence with nlogn attention mechanisms being refined. Similar story for Claude. The fun speculation is wondering about the true size of Gemini 3's internals, given the petabyte+ world size of their homefield IronwoodV7 systems and Jim Keller's public penchant for envisioning extreme MoE-like diversification across hundreds of dedicated sub-models constructed by individual teams within DeepMind.

jychang 7mo ago

Well, for one, Anthropic mostly uses Google TPUs and Amazon Inferentia2 chips, not Nvidia NVL72s. That's because... Google and Amazon are major investors in Anthropic. Secondly, you missed out the entire AI industry trend in 2024-2025, where the failure of the GPT-4.5 pretrain run and the pullback from GPT-4 to GPT-4 Turbo to GPT-4o (each of which are smaller in parameter count). GPT-4 is 1.6T, GPT-4 Turbo is generally considered 1/2 to 1/4 that, and GPT-4o is even smaller (details below) Thirdly, we KNOW that GPT-4o runs on Microsoft Maia 100 hardware with 64GB each chip, which gives a hard limit on the size of GPT-4o and tells us that it's a much smaller distilled version of GPT-4. Microsoft says each server has 4 Maia 100 chips and 256GB total. We know Microsoft uses Maia 100s to serve GPT-4o for Azure! So we know that quantized GPT-4o fits in 256GB, and GPT-4 does not fit. It's not possible to have GPT-4o be some much larger model that requires a large cluster to serve- that would drop performance below what we see in Azure. Fourthly, it is not publicly KNOWN, but leaks say that GPT-4o is 200b-300b in size, which also tells us that running GPT-4 sized models is nonsense. This matches the information from Microsoft Maia servers above. Fifthly, OpenAI Head of Research has since confirmed that o1, o3, GPT-5 use the same pretrain run as 4o, so they would be the same size.[1] That means GPT-5 is not some 1T+ model! Semianalysis confirms that the only pretrain run since 4o is 4.5, which is a ~10T model but everyone knows is a failed run. Sixthly, Amazon Bedrock and Google Vertex serves models at approximately similar memory bandwidths when calculating tokens/sec, giving 4900GB/sec for Google Vertex. Opus 4.5 aligns very well with 100b of active params. 42 tps for Claude Opus 4.6 https://openrouter.ai/anthropic/claude-opus-4.6 143 tps for GLM 4.7 (32B active parameters) https://openrouter.ai/z-ai/glm-4.7 70 tps for Llama 3.3 70B (dense model) https://openrouter.ai/meta-llama/llama-3.3-70b-instruct For GLM 4.7, that makes 143 * 32B = 4576B parameters per second, and for Llama 3.3, we get 70 * 70B = 4900B. There's calculations for Amazon Bedrock on the Opus 4.5 launch thread that compares it to gpt-oss-120b with similar conclusions. Seventhly, Anthropic distilled Opus 4/4.1 to 4.5, which is why it runs ~3x faster than Opus 4 while costing 1/3 the price in terms of API fees. Eightly, no respectable model has a sparsity below 3% these days- ridiculously low sparsity gives you Llama 4. Every single cutting edge model are around 3-5% sparsity. Knowing the active param count for Opus 4.5 gives you a very good estimate of total param count. The entire AI industry is moving AWAY from multi-trillion-parameter models. Everything is about increasing efficiency with the amount of parameters you have, not hyperscaling like GPT-4.5 which was shown to be a bad way forward. Nobody thinks Opus 4.5 is bigger than around 2T in size (so not 10T). Opus 4/4.1 may have been ~6T, but that's it. Any guess of 10T or above is patently ridiculous for both Opus 4/4.1 and Opus 4.5. [1] https://x.com/petergostev/status/1995744289079656834 https://x.com/petergostev/status/1995744289079656834