Can Mistral Small 3.1 24B run on RX 7700 XT 12GB?

Q: What should I upgrade first if Mistral Small 3.1 24B feels slow on RX 7700 XT 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

B69Good

Estimated from fit model

Mistral Small 3.1 24B needs ~13.9 GB VRAM. RX 7700 XT 12GB has 12.0 GB. With Q2_K quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

Mistral Small 3.1 24B at Q4_K_M needs 19.2 GB — too much for RX 7700 XT 12GB (12.0 GB). Runs at Q2_K (13.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.2 GB, exceeds 12.0 GB available

19.2 GB required12.0 GB available

160% VRAM needed

7.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.3 tok/s

TTFT

36402 ms

Safe context

Memory

19.2 GB / 12.0 GB

Offload

40%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMistral Small 3.1 24B on RX 7700 XT 12GB
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: 5.3 tok/s decode · 36.4s TTFT (warm) · 13 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.1 tok/s	17289 ms	4K
Coding	F	Too heavy	5.3 tok/s	36402 ms	4K
Agentic Coding	F	Too heavy	4.1 tok/s	68137 ms	4K
Reasoning	F	Too heavy	5.3 tok/s	43021 ms	4K
RAG	F	Too heavy	4.1 tok/s	85171 ms	4K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	F0
Q3_K_S	3	11.8 GB	Low	F0
NVFP4	4

Get started

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

Run

ollama run mistral-small:24b

Upgrade options

Hardware that runs Mistral Small 3.1 24B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+8)800 GB/s (+368)

Makes the model fit on the accelerator instead of staying completely out of reach.35.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+12)960 GB/s (+528)

Makes the model fit on the accelerator instead of staying completely out of reach.50.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+20)640 GB/s (+208)

Makes the model fit on the accelerator instead of staying completely out of reach.27.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Frequently asked questions

Can RX 7700 XT 12GB run Mistral Small 3.1 24B?

Yes, RX 7700 XT 12GB can run Mistral Small 3.1 24B at Q2_K quantization (Very compromised (needs ~1.3 GB host RAM)). The recommended Q4_K_M requires 19.2 GB which exceeds available memory, but at Q2_K it needs only 13.9 GB. Expected decode speed: 13.9 tok/s.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 19.2 GB at Q4_K_M quantization. On RX 7700 XT 12GB, it fits at Q2_K using 13.9 GB.

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

The recommended quantization is Q4_K_M, but on RX 7700 XT 12GB the best fitting quantization is Q2_K, which uses 13.9 GB.

What speed will Mistral Small 3.1 24B run at on RX 7700 XT 12GB?

On RX 7700 XT 12GB, Mistral Small 3.1 24B achieves approximately 13.9 tokens per second decode speed with a time-to-first-token of 13903ms using Q2_K quantization.

Can RX 7700 XT 12GB run Mistral Small 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on RX 7700 XT 12GB receives a F grade with 5.3 tok/s and 4K context.

What context window can Mistral Small 3.1 24B use on RX 7700 XT 12GB?

On RX 7700 XT 12GB, Mistral Small 3.1 24B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 7700 XT 12GB See all hardware for Mistral Small 3.1 24B

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