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

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
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	29.5 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	12.8 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	12.8 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	30.5 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	17.7 tok/s

Frequently asked questions

See all results for NVIDIA L4 24GB See all hardware for Devstral Small 2 24B Instruct

URL: https://willitrunai.com/can-run/devstral-small-2-24b-on-l4-24gb

⇱ Devstral Small 2 24B Instruct on NVIDIA L4 24GB? TIGHT FIT