Can Mistral Small 3.2 24B run on NVIDIA A100 80GB?

YES — Runs Great

A83Great

Estimated from fit model

Mistral Small 3.2 24B needs ~26.3 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~126 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.3 GB, 125.8 tok/s, Runs well

26.3 GB required80.0 GB available

33% VRAM used

Fit status

Runs well

Decode

125.8 tok/s

TTFT

1539 ms

Safe context

131K

Memory

26.3 GB / 80.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B on NVIDIA A100 80GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 125.8 tok/s decode · 1.5s TTFT (warm) · 314 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	125.8 tok/s	840 ms	131K
Coding	A	Runs well	125.8 tok/s	1539 ms	131K
Agentic Coding	A	Runs well	125.8 tok/s	2239 ms	131K
Reasoning	A	Runs well	125.8 tok/s	1819 ms	131K
RAG	A	Runs well	125.8 tok/s	2799 ms	131K

Inference speed

Mistral Small 3.2 24B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mistral Small 3.2 24B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Mistral Small 3.2 24B (24B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A74
Q3_K_S	3	11.8 GB	Low	A74
NVFP4	4	13.4 GB	Medium	A75
Q4_K_M	4	14.6 GB	Medium	A75
Q5_K_M	5	17.3 GB	High	A75
Q6_K	6	19.7 GB	High	A76
Q8_0	8	25.7 GB	Very High	A77
F16Best for your GPU	16	49.2 GB	Maximum	A82

Get started

Copy-paste commands to run Mistral Small 3.2 24B on your machine.

Run

ollama run mistral-small3.2

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	259 tok/s
Qwen 3.5 27B	27B	S	112.3 tok/s
Qwen 3.6 27B	27B	S	112.7 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Mistral Small 3.2 24B?

Yes, NVIDIA A100 80GB can run Mistral Small 3.2 24B with a A grade (Runs well). Expected decode speed: 125.8 tok/s.

How much VRAM does Mistral Small 3.2 24B need?

Mistral Small 3.2 24B (24B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.2 24B?

The recommended quantization for Mistral Small 3.2 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.2 24B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Mistral Small 3.2 24B achieves approximately 125.8 tokens per second decode speed with a time-to-first-token of 1539ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Mistral Small 3.2 24B for coding?

For coding workloads, Mistral Small 3.2 24B on NVIDIA A100 80GB receives a A grade with 125.8 tok/s and 131K context.

What context window can Mistral Small 3.2 24B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Mistral Small 3.2 24B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Mistral Small 3.2 24B

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