Can Devstral Small 2 24B Instruct run on Intel Arc A770 16GB?

YES — With NVFP4

A79Great

○Estimated from fit model

Devstral Small 2 24B Instruct needs ~18.4 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With NVFP4 quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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 19.6 GB — too much for Intel Arc A770 16GB (16.0 GB). Runs at NVFP4 (18.4 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.6 GB, exceeds 16.0 GB available

19.6 GB required16.0 GB available

123% VRAM needed

3.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.1 tok/s

TTFT

21342 ms

Safe context

Memory

19.6 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 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 Intel Arc A770 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: 9.1 tok/s decode · 21.3s TTFT (warm) · 23 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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~1.9 GB host RAM)	10.4 tok/s	10166 ms	4K
Coding	F	Too heavy	9.1 tok/s	21342 ms	4K
Agentic Coding	F	Too heavy	7.1 tok/s	39754 ms	4K
Reasoning	F	Too heavy	9.1 tok/s	25222 ms	4K
RAG	F	Too heavy	7.1 tok/s	49692 ms	4K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on Intel Arc A770 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
NVFP4	4	13.4 GB	Medium	F0
Q4_K_M	4	14.6 GB	Medium	F0
Q5_K_M	5	17.3 GB	High	F0
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

Upgrade options

Hardware that runs Devstral Small 2 24B Instruct well

👁 Intel
Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.18.1 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.

~$599 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap

32 GB VRAM (+16)1792 GB/s (+1232)

Makes the model fit on the accelerator instead of staying completely out of reach.62 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.

~$1,999 MSRP

👁 Intel
Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+112)3200 GB/s (+2640)

Makes the model fit on the accelerator instead of staying completely out of reach.148 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.

~$15,000 MSRP

👁 Intel
Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3140)

Makes the model fit on the accelerator instead of staying completely out of reach.190.2 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.

~$15,000 MSRP

Frequently asked questions

See all results for Intel Arc A770 16GB See all hardware for Devstral Small 2 24B Instruct

URL: https://willitrunai.com/can-run/devstral-small-2-24b-on-arc-a770-16gb

⇱ Devstral Small 2 24B Instruct on Intel Arc A770 16GB? No — …

Can Devstral Small 2 24B Instruct run on Intel Arc A770 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