Unverified50% confidenceTradeoffExact time
AirLLM 以推理速度为代价,每生成一个 token 耗时数秒乃至数十秒,远慢于高端 GPU 全量加载的每秒数十 token
1
Sources
50%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
Related Claims
UnverifiedMistral uses latent cache technology to compress an entire trajectory into a single training Token input, which reportedly can save approximately 22 times the computational overhead.66% similarUnverifiedBased on the MLX framework, top-configured Mac models can achieve a generation speed of approximately 20 tokens/second when running the Llama-3-70B quantized model.65% similarUnverifiedIf the quantized model weights are 150 GB, reading the weights once at a bandwidth of 273 GB/s takes approximately 0.55 seconds, giving a theoretical single-token generation speed of approximately 1.8 tokens/s (excluding computation latency and cross-node communication overhead).64% similarUnverifiedAirLLM 处理长上下文时需在系统内存中维护完整的 KV Cache,通常建议配备 64GB 以上的 RAM64% similarUnverifieddecode阶段的生成速度近似等于内存带宽÷模型大小,例如在内存带宽273GB/s的M4 Pro上运行4GB的Q4量化7B模型理论最大生成速度约68 tokens/s63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/563157API
curl https://kongchang.com/api/v1/knowledge/claims/563157MCP
get_claim(id=563157)