Can Mistral Small 3.1 24B run on NVIDIA GB200 192GB?

YES — Runs Great

A77Great

Estimated from fit model

Mistral Small 3.1 24B needs ~37.5 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~336 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) — 37.5 GB, 336.0 tok/s, Runs well

37.5 GB required192.0 GB available

20% VRAM used

Fit status

Runs well

Decode

336.0 tok/s

TTFT

576 ms

Safe context

131K

Memory

37.5 GB / 192.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsMistral Small 3.1 24B on NVIDIA GB200 192GB
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: 336.0 tok/s decode · 576ms TTFT (warm) · 840 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	336.0 tok/s	350 ms	131K
Coding	A	Runs well	336.0 tok/s	576 ms	131K
Agentic Coding	A	Runs well	336.0 tok/s	838 ms	131K
Reasoning	A	Runs well	336.0 tok/s	681 ms	131K
RAG	A	Runs well	336.0 tok/s	1048 ms	131K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	B68
Q3_K_S	3	11.8 GB	Low	B68
NVFP4	4	13.4 GB	Medium	B68
Q4_K_M	4	14.6 GB	Medium	B68
Q5_K_M	5	17.3 GB	High	B68
Q6_K	6	19.7 GB	High	B69
Q8_0	8	25.7 GB	Very High	B69
F16Best for your GPU	16	49.2 GB	Maximum	A72

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 GB200 192GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	97.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	1016.1 tok/s
Qwen 3.5 27B	27B	S	378 tok/s
Qwen 3.6 27B	27B	S	378 tok/s
Qwen 3.5 122B A10B	122B	S	270.2 tok/s

Frequently asked questions

Can NVIDIA GB200 192GB run Mistral Small 3.1 24B?

Yes, NVIDIA GB200 192GB can run Mistral Small 3.1 24B with a A grade (Runs well). Expected decode speed: 336.0 tok/s.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 37.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.1 24B?

The recommended quantization for Mistral Small 3.1 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.1 24B run at on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Mistral Small 3.1 24B achieves approximately 336.0 tokens per second decode speed with a time-to-first-token of 576ms using Q4_K_M quantization.

Can NVIDIA GB200 192GB run Mistral Small 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on NVIDIA GB200 192GB receives a A grade with 336.0 tok/s and 131K context.

What context window can Mistral Small 3.1 24B use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Mistral Small 3.1 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 GB200 192GB See all hardware for Mistral Small 3.1 24B

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