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Pick the right model for your GPU — in seconds.

What GPU do you need to run LFM2.5 24B A2B?

23.97B params 128K max context Tool useReasoning

The smallest GPU memory that runs LFM2.5 24B A2B well is 16 GB (Q4_K_M, e.g. RTX PRO 2000 Blackwell). 72 of 132 GPUs run it well. It's a mixture-of-experts model, so all weights must fit in memory but speed depends only on the active experts.

SWE-bench Verified 51

VRAM needed per quantization

QuantizationWeights16K context32K contextFull 128K
Q4_K_M13.5 GB13.8 GB14.1 GB16.0 GB
Q8_023.7 GB24.0 GB24.3 GB26.2 GB

Weights plus fp16 KV cache. Leave ~1 GB headroom for the inference engine.

Cheapest GPUs that can run LFM2.5 24B A2B

Lowest launch MSRP among GPUs that run its smallest quantization (Q4_K_M) well, with at least 16K context.

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GPUs that can run LFM2.5 24B A2B

Best quantization that fits on each GPU, with the context and speed it gets. For chips with several memory sizes, the smallest that works is shown.

NVIDIA

AMD

Intel

Apple

  • M1 16 GB Q4_K_M 128K context · ~29 tok/s
  • M1 Pro 16 GB Q4_K_M 128K context · ~84 tok/s
  • M2 16 GB Q4_K_M 128K context · ~42 tok/s
  • M2 Pro 16 GB Q4_K_M 128K context · ~84 tok/s
  • M3 16 GB Q4_K_M 128K context · ~42 tok/s
  • M4 16 GB Q4_K_M 128K context · ~50 tok/s
  • M5 16 GB Q4_K_M 128K context · ~64 tok/s
  • M6 16 GB Q4_K_M 128K context · ~64 tok/s
  • M3 Pro 18 GB Q4_K_M 128K context · ~63 tok/s
  • M4 Pro 24 GB Q4_K_M 128K context · ~115 tok/s
  • M5 Pro 24 GB Q4_K_M 128K context · ~129 tok/s
  • M1 Max 32 GB Q8_0 128K context · ~117 tok/s
  • M2 Max 32 GB Q8_0 128K context · ~117 tok/s
  • M3 Max 36 GB Q8_0 128K context · ~87 tok/s
  • M4 Max 36 GB Q8_0 128K context · ~120 tok/s
  • M5 Max 36 GB Q8_0 128K context · ~134 tok/s
  • M1 Ultra 64 GB Q8_0 128K context · ~233 tok/s
  • M2 Ultra 64 GB Q8_0 128K context · ~233 tok/s
  • M3 Ultra 96 GB Q8_0 128K context · ~239 tok/s
  • M5 Ultra 96 GB Q8_0 128K context · ~350 tok/s
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