Unverified50% confidenceBenchmarkExact time
Testing with a 4-bit quantized Qwen2.5 7B model on an M2 Mac mini with 16 GB unified memory, throughput dropped from 16.8 tokens/s without batching to 8.7 tokens/s with batching.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedQuantprobe claims to enable a machine with only 6GB of VRAM and 16GB of RAM to run 30B-class models at 22 tokens/s.76% similarUnverifiedThe 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.75% similarUnverifiedThe article uses a 125-billion-parameter Qwen model with a file size of 105GB; this task could only be run on the Mac Studio due to the model's size, as the 32GB Mac mini could not accommodate it.75% similarVerified一台配备 16GB 内存的普通 Mac 借助 GGUF 量化格式可流畅运行 7B 至 13B 参数的模型73% similarUnverifiedWhen running 4-bit DeepSeek R1 on the M3 Ultra, a 1,000-token context generates approximately 19 tokens per second using 392GB, dropping to approximately 15 tokens per second using 410GB at a 16,000-token context.72% similar
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