Unverified50% confidenceFactExact time
在手机上运行一个3B参数的LLM仍面临约6GB内存占用和数百毫秒的首token延迟
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
8/26/2026
First Seen
Valid until: 11/24/2026
Sources
Related Entities
Related Claims
UnverifiedThe 4-bit quantized GGUF format version of llama.cpp enables the Llama 3.1 70B model to run on an Apple M2 Ultra with 64GB unified memory at an inference speed of approximately 20–30 tokens/second.70% similarVerifiedModern CPU L1 cache access latency is approximately 0.5 nanoseconds, L2 approximately 4 nanoseconds, and a single main memory access requires 60–100 nanoseconds.69% similarPartially VerifiedFor the 4-bit quantized version of a 7B parameter model, at least 8GB of memory and a modern multi-core CPU are typically required for smooth operation, with inference speeds of approximately 10–30 tokens per second68% 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).68% similarUnverifiedSmall models (1B–7B parameters) used for text completion tasks can run smoothly on Macs with 8 GB or more of unified memory, with inference latency kept within a few hundred milliseconds.68% similar
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