What Models?

Pick the right model for your GPU — in seconds.

What GPU do you need to run Llama 3.2 3B?

3.21B params 128K max context Tool use

The smallest GPU memory that runs Llama 3.2 3B well is 4 GB (Q4_K_M, e.g. T1000). 131 of 132 GPUs run it well.

MMLU 63.4

VRAM needed per quantization

QuantizationWeights16K context32K contextFull 128K
Q4_K_M2.0 GB3.7 GB5.4 GB15.7 GB
Q8_03.4 GB5.1 GB6.8 GB17.1 GB
fp166.4 GB8.1 GB9.9 GB20.1 GB

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

Cheapest GPUs that can run Llama 3.2 3B

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 Llama 3.2 3B

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 8 GB Q8_0 42K context · ~15 tok/s
  • M2 8 GB Q8_0 42K context · ~23 tok/s
  • M3 8 GB Q8_0 42K context · ~23 tok/s
  • M1 Pro 16 GB fp16 89K context · ~27 tok/s
  • M2 Pro 16 GB fp16 89K context · ~27 tok/s
  • M4 16 GB fp16 89K context · ~16 tok/s
  • M5 16 GB fp16 89K context · ~21 tok/s
  • M6 16 GB fp16 89K context · ~21 tok/s
  • M3 Pro 18 GB fp16 108K context · ~20 tok/s
  • M4 Pro 24 GB fp16 128K context · ~37 tok/s
  • M5 Pro 24 GB fp16 128K context · ~41 tok/s
  • M1 Max 32 GB fp16 128K context · ~54 tok/s
  • M2 Max 32 GB fp16 128K context · ~54 tok/s
  • M3 Max 36 GB fp16 128K context · ~40 tok/s
  • M4 Max 36 GB fp16 128K context · ~55 tok/s
  • M5 Max 36 GB fp16 128K context · ~62 tok/s
  • M1 Ultra 64 GB fp16 128K context · ~108 tok/s
  • M2 Ultra 64 GB fp16 128K context · ~108 tok/s
  • M3 Ultra 96 GB fp16 128K context · ~110 tok/s
  • M5 Ultra 96 GB fp16 128K context · ~162 tok/s
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