Can Devstral Small 2 24B Instruct run on Radeon Pro W7800 32GB?

YES — Runs Great

S93Excellent

Estimated from fit model

Devstral Small 2 24B Instruct needs ~21.2 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 21.2 GB, 25.0 tok/s, Runs well

21.2 GB required32.0 GB available

66% VRAM used

Fit status

Runs well

Decode

25.0 tok/s

TTFT

7758 ms

Safe context

87K

Memory

21.2 GB / 32.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on Radeon Pro W7800 32GB
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: 25.0 tok/s decode · 7.8s TTFT (warm) · 62 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	S	Runs well	25.0 tok/s	4232 ms	87K
Coding	S	Runs well	25.0 tok/s	7758 ms	87K
Agentic Coding	S	Runs well	25.0 tok/s	11285 ms	87K
Reasoning	S	Runs well	25.0 tok/s	9169 ms	87K
RAG	S	Runs well	25.0 tok/s	14106 ms	87K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S87
Q3_K_S	3	11.8 GB	Low	S88
NVFP4	4	13.4 GB	Medium	S89
Q4_K_M	4	14.6 GB	Medium	S90
Q5_K_M	5	17.3 GB	High	S91
Q6_K	6	19.7 GB	High	S90
Q8_0Best for your GPU	8	25.7 GB	Very High	S90
F16	16	49.2 GB	Maximum	F0

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 Radeon Pro W7800 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	51.4 tok/s
Qwen 3.5 27B	27B	S	22.3 tok/s
Qwen 3.6 27B	27B	S	16.9 tok/s
Qwen 3.6 35B A3B	35B	S	43.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.1 tok/s

Frequently asked questions

Can Radeon Pro W7800 32GB run Devstral Small 2 24B Instruct?

Yes, Radeon Pro W7800 32GB can run Devstral Small 2 24B Instruct with a S grade (Runs well). Expected decode speed: 25.0 tok/s.

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

Devstral Small 2 24B Instruct (24B parameters) requires approximately 21.2 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 Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Devstral Small 2 24B Instruct achieves approximately 25.0 tokens per second decode speed with a time-to-first-token of 7758ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on Radeon Pro W7800 32GB receives a S grade with 25.0 tok/s and 87K context.

What context window can Devstral Small 2 24B Instruct use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Devstral Small 2 24B Instruct can safely use up to 87K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for Radeon Pro W7800 32GB 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-radeon-pro-w7800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

25.0 tok/s

4232 ms

87K

Coding

Runs well

25.0 tok/s

7758 ms

87K

Agentic Coding

Runs well

25.0 tok/s

11285 ms

87K

Reasoning

Runs well

25.0 tok/s

9169 ms

87K

RAG

Runs well

25.0 tok/s

14106 ms

87K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S87
Q3_K_S	3	11.8 GB	Low	S88
NVFP4	4	13.4 GB	Medium	S89
Q4_K_M	4	14.6 GB	Medium	S90
Q5_K_M	5	17.3 GB	High	S91
Q6_K	6	19.7 GB	High	S90
Q8_0Best for your GPU	8	25.7 GB	Very High	S90
F16	16	49.2 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

51.4 tok/s

Qwen 3.5 27B

27B

22.3 tok/s

Qwen 3.6 27B

27B

16.9 tok/s

Qwen 3.6 35B A3B

35B

43.2 tok/s

Qwen3-VL 30B A3B Instruct

30B

53.1 tok/s