Unverified60% confidenceBenchmarkExact time
Apple M2/M3 chips adopt a Unified Memory Architecture (UMA), and llama.cpp can achieve approximately 30–50 tokens/second inference speed for 7B models via the Metal API.
2
Sources
60%
Confidence
Long-term
Relevance
7/6/2026
First Seen
Sources
Ollama: The 175K-Star Tool for Running Local LLMs with One Command
bilibili比比蜡卜7/1/2026
Related Claims
UnverifiedThe llama.cpp project is optimized for Apple Silicon, making it possible to smoothly run 7B and even 13B quantized models on a Mac with 24GB unified memory.76% similarUnverifiedIn on-device inference, a 4-bit quantized 7B-class model typically needs only 4–6 GB of memory on Apple unified memory, and inference speed can reach dozens of tokens per second.73% similarVerifiedFor 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 second73% similar
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