Can Devstral Small 2 24B Instruct run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

S93Excellent

Estimated from fit model

Devstral Small 2 24B Instruct needs ~23.1 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~83 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) — 23.1 GB, 82.9 tok/s, Runs well

23.1 GB required48.0 GB available

48% VRAM used

Fit status

Runs well

Decode

82.9 tok/s

TTFT

2335 ms

Safe context

179K

Memory

23.1 GB / 48.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on RTX PRO 5000 Blackwell 48GB
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: 82.9 tok/s decode · 2.3s TTFT (warm) · 207 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	82.9 tok/s	1274 ms	179K
Coding	S	Runs well	82.9 tok/s	2335 ms	179K
Agentic Coding	S	Runs well	82.9 tok/s	3397 ms	179K
Reasoning	S	Runs well	82.9 tok/s	2760 ms	179K
RAG	S	Runs well	82.9 tok/s	4246 ms	179K

Inference speed

Devstral Small 2 24B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Devstral Small 2 24B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_S	3	11.8 GB	Low	A85
NVFP4	4	13.4 GB	Medium	S85
Q4_K_M	4	14.6 GB	Medium	S86
Q5_K_M	5	17.3 GB	High	S87
Q6_K	6	19.7 GB	High	S87
Q8_0Best for your GPU	8	25.7 GB	Very High	S89
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 RTX PRO 5000 Blackwell 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	170.7 tok/s
Qwen 3.5 27B	27B	S	74 tok/s
Qwen 3.6 27B	27B	S	74.3 tok/s
Qwen 3.6 35B A3B	35B	S	143.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	176.6 tok/s

Frequently asked questions

Can RTX PRO 5000 Blackwell 48GB run Devstral Small 2 24B Instruct?

Yes, RTX PRO 5000 Blackwell 48GB can run Devstral Small 2 24B Instruct with a S grade (Runs well). Expected decode speed: 82.9 tok/s.

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

Devstral Small 2 24B Instruct (24B parameters) requires approximately 23.1 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 RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Devstral Small 2 24B Instruct achieves approximately 82.9 tokens per second decode speed with a time-to-first-token of 2335ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on RTX PRO 5000 Blackwell 48GB receives a S grade with 82.9 tok/s and 179K context.

What context window can Devstral Small 2 24B Instruct use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Devstral Small 2 24B Instruct can safely use up to 179K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for Devstral Small 2 24B Instruct

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

82.9 tok/s

1274 ms

179K

Coding

Runs well

82.9 tok/s

2335 ms

179K

Agentic Coding

Runs well

82.9 tok/s

3397 ms

179K

Reasoning

Runs well

82.9 tok/s

2760 ms

179K

RAG

Runs well

82.9 tok/s

4246 ms

179K

Devstral Small 2 24B Instruct inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

88.2

Fits

RTX 4090 24GB

24 GB

Q4_K_M

56.3

Tight

RX 7900 XTX 24GB

24 GB

Q4_K_M

50.8

Tight

RTX 3090 24GB

24 GB

Q4_K_M

48.1

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

40.9

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

36.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

36.8

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

34.1

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

32.3

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

23.2

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

21.3

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

17.6

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

16.2

Fits

RTX 4070 12GB

12 GB

Q4_K_M

7.5

Too big

RTX 3060 12GB

12 GB

Q4_K_M

4.7

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.2

Too big

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_S	3	11.8 GB	Low	A85
NVFP4	4	13.4 GB	Medium	S85
Q4_K_M	4	14.6 GB	Medium	S86
Q5_K_M	5	17.3 GB	High	S87
Q6_K	6	19.7 GB	High	S87
Q8_0Best for your GPU	8	25.7 GB	Very High	S89
F16	16	49.2 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

170.7 tok/s

Qwen 3.5 27B

27B

74 tok/s

Qwen 3.6 27B

27B

74.3 tok/s

Qwen 3.6 35B A3B

35B

143.5 tok/s

Qwen3-VL 30B A3B Instruct

30B

176.6 tok/s