Unverified50% confidenceBenchmarkExact time
Collapse推理速度：CPU上单条10-token上下文约0.23毫秒，MPS在batch 1024下约230万token/秒，最近邻查询约0.48毫秒
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
No MLP or Attention Required: How Point-Attractor Dynamics Learn Word Embeddings
redditr/learnmachinelearning7/10/2026
Related Claims
UnverifiedOn high-performance laptops, quantized Gemma models can achieve token generation speeds of 20–50 tokens per second; pure CPU inference is approximately 5–15 tokens/second, and INT4 quantization on NVIDIA RTX 4060/4070 can boost inference speed by 3–5 times.72% similarUnverifiedWhen running at 1-bit precision on an M2 Max equipped with 96GB of VRAM, generation speed for simple tasks is consistently around 28–30 tokens/second72% similarUnverifiedHuawei Cloud's ASS zero-zone computing cluster supports 100,000-card-scale computing power, compresses Token generation latency to within 10 milliseconds, and achieves computing power of 2001 PFLOPS.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/491364API
curl https://kongchang.com/api/v1/knowledge/claims/491364MCP
get_claim(id=491364)