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Run LLM Locally

Mixtral 8x7B Instruct on the A100 80GB

With 80 GB of VRAM, the A100 80GB runs Mixtral 8x7B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 27.5 GB needed). At Q8_0 it fits entirely.

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Mixtral 8x7B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 27.5 GB and fits in the 77.6 GB available, with 50.1 GB to spare.

Your hardware
A100 80GB · 64 GB RAM
Estimated memory
~27.5 GB estimate
VRAM (usable memory)
77.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
109–163 tok/s estimate
Largest context in fast memory
32K tokens
Confidence: low

Based on: memory bandwidth (1,935 GB/s) and model size

Memory breakdown

~27.5 GB (26.8 GB–28.3 GB) needed · estimate

Fits entirely in VRAM · 50.1 GB left

  • Model weights24.6 GB
  • KV cache1 GB
  • Runtime overhead0.6 GB
  • Safety margin1.3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet
  • mixture-of-experts: speed depends on expert routing

Other quantizations on the A100 80GB

Mixtral 8x7B Instruct on A100 80GB + 64 GB RAM · 8K context
Mixtral 8x7B Instruct Q8_08K~50.2 GB · 70–99 tok/s estimateExcellent
Mixtral 8x7B Instruct Q6_K8K~39.2 GB · 85–123 tok/s estimateExcellent
Mixtral 8x7B Instruct Q5_K_M8K~33.2 GB · 96–141 tok/s estimateExcellent
Mixtral 8x7B Instruct Q4_K_M8K~27.5 GB · 109–163 tok/s estimateExcellent
Mixtral 8x7B Instruct IQ4_XS8K~27.1 GB · 110–165 tok/s estimateExcellent
Mixtral 8x7B Instruct Q3_K_M8K~21.6 GB · 128–197 tok/s estimateExcellent

Alternatives

GPUs that run Mixtral 8x7B Instruct Q4_K_M entirely in VRAM

Other models that fit the A100 80GB

Hardware that runs Mixtral 8x7B Instruct Q4_K_M entirely in memory

Prices and links follow the market you choose.

GeForce RTX 5090

NVIDIA

CompatibleRuns Mixtral 8x7B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

Radeon PRO W7900

AMD

ExcellentRuns Mixtral 8x7B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

MacBook Pro 14 M3 Max

Apple Silicon

ExcellentRuns Mixtral 8x7B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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AMD Ryzen AI Max+ 395

Unified memory PC

ExcellentRuns Mixtral 8x7B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the A100 80GB run Mixtral 8x7B Instruct?
The model needs about 27.5 GB and fits in the 77.6 GB available, with 50.1 GB to spare.
Which quantization should I use on the A100 80GB?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~50.2 GB / 77.6 GB.
What is the largest context on the A100 80GB?
At Q4_K_M, about 32K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Mixtral 8x7B Instruct (A100 80GB)?
We estimate 109–163 tok/s (estimate, confidence low). Based on: memory bandwidth (1,935 GB/s) and model size.