Unverified50% confidenceOpinionExact time
MicroGPT's 10M TPS figure implies that its model parameter count is very likely in the range of tens of thousands to hundreds of thousands, with the performance bottleneck shifting from compute-bound to memory-access-bound.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
8/30/2026
First Seen
Valid until: 11/28/2026
Sources
Related Entities
Related Claims
VerifiedThe speed bottleneck in large model inference is memory bandwidth rather than insufficient computing power, as generating each token requires loading hundreds of GB of model weights from memory.66% similarUnverifiedWhen running local LLMs, VRAM capacity is often more critical than computational speed, as VRAM capacity directly determines which models can be run.65% similarUnverifiedGPT-OSS 20B运行时内存占用约10GB，显存占用约14.5GB64% similarUnverified上下文拉长后，20.7B稠密模型约两成参数溢出到内存，速度暴跌至每秒5.7个Token，而矮星几乎不受影响63% similarUnverifiedA single inference at the million-token level requires tens to hundreds of gigabytes of high-end GPU memory (A100/H100 class).63% similar
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