Expired60% confidenceBenchmarkExact time
The 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.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/16/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/14/2026(expired)
Sources
Related Claims
UnverifiedApple 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.80% similarUnverifiedThe 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.78% similarVerifiedA Mac Studio M4 Ultra equipped with 192 GB of unified memory can theoretically run complete 70B or even larger parameter models.77% similarUnverifiedOn an Apple M4 (16GB memory), running all-MiniLM-L6-v2 through ChromaDB embedded 50,000 documents in 161.6 seconds (309 per second), with no dedicated GPU required.76% similarVerifiedUnder the M5 Ultra's 1.2 TB/s bandwidth, the theoretical speed of a 100 GB quantized model is approximately 12 tokens/s, a 200 GB model approximately 6 tokens/s, and a 300 GB model approximately 4 tokens/s.76% similar
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