Can Devstral Small 2 24B Instruct run on NVIDIA L4 24GB?

YES — Tight Fit

S89Excellent

Estimated from fit model

Devstral Small 2 24B Instruct needs ~20.7 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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) — 20.7 GB, 14.3 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

14.3 tok/s

TTFT

13521 ms

Safe context

38K

Memory

20.7 GB / 24.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on NVIDIA L4 24GB
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: 14.3 tok/s decode · 13.5s TTFT (warm) · 36 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	14.3 tok/s	7375 ms	38K
Coding	S	Tight fit	14.3 tok/s	13521 ms	38K
Agentic Coding	S	Runs with offload	14.3 tok/s	19667 ms	38K
Reasoning	S	Tight fit	14.3 tok/s	15979 ms	38K
RAG	S	Runs with offload	14.3 tok/s	24583 ms	38K

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 NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S90
Q3_K_S	3	11.8 GB	Low	S92
NVFP4	4	13.4 GB	Medium	S91
Q4_K_M	4	14.6 GB	Medium	S91
Q5_K_MBest for your GPU	5	17.3 GB	High	S91
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
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 NVIDIA L4 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	29.5 tok/s
Qwen 3.5 27B	27B	S	12.8 tok/s
Qwen 3.6 27B	27B	S	12.8 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	30.5 tok/s
Qwen 3.5 35B A3B	35B	A	17.7 tok/s

Frequently asked questions

Can NVIDIA L4 24GB run Devstral Small 2 24B Instruct?

Yes, NVIDIA L4 24GB can run Devstral Small 2 24B Instruct with a S grade (Tight fit). Expected decode speed: 14.3 tok/s.

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

Devstral Small 2 24B Instruct (24B parameters) requires approximately 20.7 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Devstral Small 2 24B Instruct achieves approximately 14.3 tokens per second decode speed with a time-to-first-token of 13521ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on NVIDIA L4 24GB receives a S grade with 14.3 tok/s and 38K context.

What context window can Devstral Small 2 24B Instruct use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Devstral Small 2 24B Instruct can safely use up to 38K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB 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-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: