Can Devstral Small 2 24B Instruct run on RTX A4000 16GB?

YES — With NVFP4

A79Great

○Estimated from fit model

Devstral Small 2 24B Instruct needs ~18.7 GB VRAM. RTX A4000 16GB has 16.0 GB. With NVFP4 quantization, expect ~14 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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.

Devstral Small 2 24B Instruct at Q4_K_M needs 19.9 GB — too much for RTX A4000 16GB (16.0 GB). Runs at NVFP4 (18.7 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.9 GB, exceeds 16.0 GB available

19.9 GB required16.0 GB available

124% VRAM needed

3.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.9 tok/s

TTFT

17708 ms

Safe context

Memory

19.9 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDevstral Small 2 24B Instruct on RTX A4000 16GB
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: 10.9 tok/s decode · 17.7s TTFT (warm) · 27 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised	11.6 tok/s	9087 ms	4K
Coding	F	Too heavy	10.2 tok/s	19036 ms	4K
Agentic Coding	F	Too heavy	8.0 tok/s	35333 ms	4K
Reasoning	F	Too heavy	10.2 tok/s	22497 ms	4K
RAG	F	Too heavy	8.0 tok/s	44166 ms	4K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S92
Q3_K_SBest for your GPU	3	11.8 GB	Low	S92

Get started

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

Run

ollama run devstral-small-2

Upgrade options

Hardware that runs Devstral Small 2 24B Instruct well

👁 NVIDIA
RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.15 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+488)

Makes the model fit on the accelerator instead of staying completely out of reach.48.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+560)

Makes the model fit on the accelerator instead of staying completely out of reach.56.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

See all results for RTX A4000 16GB See all hardware for Devstral Small 2 24B Instruct

URL: https://willitrunai.com/can-run/devstral-small-2-24b-on-a4000-16gb

⇱

Can Devstral Small 2 24B Instruct run on RTX A4000 16GB?

YES — With NVFP4

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Devstral Small 2 24B Instruct well

Frequently asked questions