Can Devstral Small 2 24B Instruct run on RX 7900 XT 20GB?

Q: What should I upgrade first if Devstral Small 2 24B Instruct feels slow on RX 7900 XT 20GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

S92Excellent

Estimated from fit model

Devstral Small 2 24B Instruct needs ~20.0 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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) — 20.0 GB, 35.2 tok/s, Runs with offload

20.0 GB required20.0 GB available

100% VRAM used

Fit status

Runs with offload

Decode

35.2 tok/s

TTFT

5493 ms

Safe context

16K

Memory

20.0 GB / 20.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on RX 7900 XT 20GB
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: 35.2 tok/s decode · 5.5s TTFT (warm) · 88 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	35.2 tok/s	2996 ms	16K
Coding	S	Runs with offload	35.2 tok/s	5493 ms	16K
Agentic Coding	A	Very compromised (needs ~1.6 GB host RAM)	20.8 tok/s	13552 ms	16K
Reasoning	S	Runs with offload	35.2 tok/s	6492 ms	16K
RAG	A	Very compromised (needs ~1.6 GB host RAM)	20.8 tok/s	16940 ms

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S92
Q3_K_S	3	11.8 GB	Low	S92
NVFP4	4

Get started

Copy-paste commands to run Devstral Small 2 24B Instruct on your machine.

Run

ollama run devstral-small-2

Your hardware

More models your RX 7900 XT 20GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	40.7 tok/s
Qwen 3.5 27B	27B	A	18.3 tok/s

Frequently asked questions

Can RX 7900 XT 20GB run Devstral Small 2 24B Instruct?

Yes, RX 7900 XT 20GB can run Devstral Small 2 24B Instruct with a S grade (Runs with offload). Expected decode speed: 35.2 tok/s.

How much VRAM does Devstral Small 2 24B Instruct need?

Devstral Small 2 24B Instruct (24B parameters) requires approximately 20.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 2 24B Instruct?

The recommended quantization for Devstral Small 2 24B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 2 24B Instruct run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Devstral Small 2 24B Instruct achieves approximately 35.2 tokens per second decode speed with a time-to-first-token of 5493ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on RX 7900 XT 20GB receives a S grade with 35.2 tok/s and 16K context.

What context window can Devstral Small 2 24B Instruct use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Devstral Small 2 24B Instruct can safely use up to 16K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for Devstral Small 2 24B Instruct

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/devstral-small-2-24b-on-rx-7900-xt-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: