Can Mistral Small 3.1 24B run on NVIDIA A16 64GB?

YES — Runs Great

A78Great

○Estimated from fit model

Mistral Small 3.1 24B needs ~24.7 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 24.7 GB, 34.4 tok/s, Runs well

24.7 GB required64.0 GB available

39% VRAM used

Fit status

Runs well

Decode

34.4 tok/s

TTFT

5634 ms

Safe context

131K

Memory

24.7 GB / 64.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsMistral Small 3.1 24B on NVIDIA A16 64GB
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: 34.4 tok/s decode · 5.6s TTFT (warm) · 86 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	34.4 tok/s	3073 ms	131K
Coding	A	Runs well	34.4 tok/s	5634 ms	131K
Agentic Coding	A	Runs well	34.4 tok/s	8194 ms	131K
Reasoning	A	Runs well	34.4 tok/s	6658 ms	131K
RAG	A	Runs well	34.4 tok/s	10243 ms	131K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A72
Q3_K_S	3	11.8 GB	Low	A73
NVFP4	4	13.4 GB	Medium	A73
Q4_K_M	4	14.6 GB	Medium	A73
Q5_K_M	5	17.3 GB	High	A74
Q6_K	6	19.7 GB	High	A75
Q8_0	8	25.7 GB	Very High	A76
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 A16 64GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.7 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	30.8 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	59.5 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	73.2 tok/s

Frequently asked questions

See all results for NVIDIA A16 64GB See all hardware for Mistral Small 3.1 24B

URL: https://willitrunai.com/can-run/mistral-small-3.1-24b-on-a16-64gb

⇱ Mistral Small 3.1 24B on NVIDIA A16 64GB? YES