What Models?

Pick the right model for your GPU — in seconds.

What GPU do you need to run Gemma 4 12B?

11.96B params 256K max context VisionTool useReasoning

The smallest GPU memory that runs Gemma 4 12B well is 8 GB (Q4_K_M, e.g. GeForce RTX 5060 Ti). 120 of 132 GPUs run it well.

MMLU 77.2

VRAM needed per quantization

QuantizationWeights16K context32K contextFull 256K
Q4_K_M6.6 GB6.9 GB7.1 GB10.5 GB
Q8_011.8 GB12.0 GB12.3 GB15.6 GB

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

Cheapest GPUs that can run Gemma 4 12B

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 Gemma 4 12B

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 Q4_K_M 91K context · ~9 tok/s
  • M2 8 GB Q4_K_M 91K context · ~13 tok/s
  • M3 8 GB Q4_K_M 91K context · ~13 tok/s
  • M1 Pro 16 GB Q8_0 256K context · ~16 tok/s
  • M2 Pro 16 GB Q8_0 256K context · ~16 tok/s
  • M4 16 GB Q8_0 256K context · ~9 tok/s
  • M5 16 GB Q8_0 256K context · ~12 tok/s
  • M6 16 GB Q8_0 256K context · ~12 tok/s
  • M3 Pro 18 GB Q8_0 256K context · ~12 tok/s
  • M4 Pro 24 GB Q8_0 256K context · ~21 tok/s
  • M5 Pro 24 GB Q8_0 256K context · ~24 tok/s
  • M1 Max 32 GB Q8_0 256K context · ~31 tok/s
  • M2 Max 32 GB Q8_0 256K context · ~31 tok/s
  • M3 Max 36 GB Q8_0 256K context · ~23 tok/s
  • M4 Max 36 GB Q8_0 256K context · ~32 tok/s
  • M5 Max 36 GB Q8_0 256K context · ~36 tok/s
  • M1 Ultra 64 GB Q8_0 256K context · ~63 tok/s
  • M2 Ultra 64 GB Q8_0 256K context · ~63 tok/s
  • M3 Ultra 96 GB Q8_0 256K context · ~64 tok/s
  • M5 Ultra 96 GB Q8_0 256K context · ~94 tok/s
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