Can Mistral Small 3.1 24B run on NVIDIA A100 80GB?

YES — Runs Great

A80Great

○Estimated from fit model

Mistral Small 3.1 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 3.1 24B on NVIDIA A100 80GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
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

Quantization options

How Mistral Small 3.1 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	A71
Q3_K_S	3	11.8 GB	Low	A72
NVFP4	4	13.4 GB	Medium	A72
Q4_K_M	4	14.6 GB	Medium	A72
Q5_K_M	5	17.3 GB	High	A73
Q6_K	6	19.7 GB	High	A73
Q8_0	8	25.7 GB	Very High	A74
F16Best for your GPU	16	49.2 GB	Maximum	A79

Get started

Copy-paste commands to run Mistral Small 3.1 24B on your machine.

Run

ollama run mistral-small:24b

Your hardware

More models your NVIDIA A100 80GB can run

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

Frequently asked questions

See all results for NVIDIA A100 80GB See all hardware for Mistral Small 3.1 24B

URL: https://willitrunai.com/can-run/mistral-small-3.1-24b-on-a100-80gb

⇱ Mistral Small 3.1 24B on NVIDIA A100 80GB? YES