Unverified50% confidenceFactExact time
Running LFM2.5-24B-A2B on macOS + Apple Silicon via MLX (mlx-lm>=0.32.0) can achieve over 150 tokens/second within 16GB of memory.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/14/2026
First Seen
Valid until: 1/12/2027
Sources
Related Entities
Related Claims
UnverifiedOn Apple Silicon, the natively recommended approach is MLX 4-bit affine quantization (group size 64), with a 4-bit MLX build occupying approximately 15 GB of unified memory.76% similarUnverifiedLFM2.5-24B-A2B achieves a decoding speed of 184 tokens/s on an Apple M5 Max.75% similarUnverifiedThe blogger ran Gemma 3 12B with no difficulty on a Mac Mini with 16 GB of RAM and an M2 Pro chip.74% similarUnverifiedOllama local deployment measured throughput: MacBook M2 16GB approximately 15 tokens/second, RTX 3060 12GB approximately 45, RTX 4090 24GB approximately 70, 8GB laptop approximately 574% similarUnverifiedLocal inference is constrained by the user's hardware: a 7B-parameter quantized model (such as the Q4 version of Llama 3 8B) has a file size of approximately 4–5 GB, can reach 20–40 tokens per second on a Mac with an Apple M-series chip, and may drop to 3–5 tokens per second on a laptop with only an integrated GPU and 8 GB of RAM.73% similar
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