What Models?

Pick the right model for your GPU — in seconds.

What GPU do you need to run Llama 3.1 8B?

8.03B params 128K max context Tool use

The smallest GPU memory that runs Llama 3.1 8B well is 8 GB (Q4_K_M, e.g. GeForce RTX 5060 Ti). 120 of 132 GPUs run it well and 8 more are a tight fit.

MMLU 69.4 SWE-bench Verified 6.2

VRAM needed per quantization

QuantizationWeights16K context32K contextFull 128K
Q4_K_M4.9 GB6.9 GB8.8 GB20.5 GB
Q8_08.5 GB10.5 GB12.4 GB24.2 GB
fp1616.1 GB18.0 GB20.0 GB31.7 GB

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

Cheapest GPUs that can run Llama 3.1 8B

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

As an Amazon Associate, I earn from qualifying purchases.

GPUs that can run Llama 3.1 8B

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 25K context · ~11 tok/s
  • M2 8 GB Q4_K_M 25K context · ~17 tok/s
  • M3 8 GB Q4_K_M 25K context · ~17 tok/s
  • M1 Pro 16 GB Q8_0 61K context · ~21 tok/s
  • M2 Pro 16 GB Q8_0 61K context · ~21 tok/s
  • M4 16 GB Q8_0 61K context · ~13 tok/s
  • M5 16 GB Q8_0 61K context · ~16 tok/s
  • M6 16 GB Q8_0 61K context · ~16 tok/s
  • M3 Pro 18 GB Q8_0 77K context · ~16 tok/s
  • M4 Pro 24 GB fp16 65K context · ~16 tok/s
  • M5 Pro 24 GB fp16 65K context · ~18 tok/s
  • M1 Max 32 GB fp16 128K context · ~23 tok/s
  • M2 Max 32 GB fp16 128K context · ~23 tok/s
  • M3 Max 36 GB fp16 128K context · ~18 tok/s
  • M4 Max 36 GB fp16 128K context · ~24 tok/s
  • M5 Max 36 GB fp16 128K context · ~27 tok/s
  • M1 Ultra 64 GB fp16 128K context · ~47 tok/s
  • M2 Ultra 64 GB fp16 128K context · ~47 tok/s
  • M3 Ultra 96 GB fp16 128K context · ~48 tok/s
  • M5 Ultra 96 GB fp16 128K context · ~70 tok/s
Check your exact setup in the full tool →