Will It Run AI

Can Devstral Small 2 24B Instruct run on Intel Arc Pro B60 24GB?

YES — Tight Fit

S90Excellent
Estimated from fit model

Devstral Small 2 24B Instruct needs ~20.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
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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) — 20.4 GB, 18.1 tok/s, Tight fit
20.4 GB required24.0 GB available
85% VRAM used

Fit status

Tight fit

Decode

18.1 tok/s

TTFT

10707 ms

Safe context

40K

Memory

20.4 GB / 24.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on Intel Arc Pro B60 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: 18.1 tok/s decode · 10.7s TTFT (warm) · 45 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well18.1 tok/s5840 ms40K
CodingSTight fit18.1 tok/s10707 ms40K
Agentic CodingSRuns with offload18.1 tok/s15574 ms40K
ReasoningSTight fit18.1 tok/s12654 ms40K
RAGSRuns with offload18.1 tok/s19468 ms40K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowS90
Q3_K_S
3
11.8 GB
LowS92
NVFP4
4
13.4 GB
MediumS91
Q4_K_M
4
14.6 GB
MediumS91
Q5_K_MBest for your GPU
5
17.3 GB
HighS91
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

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 Intel Arc Pro B60 24GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BS37.2 tok/s
AlibabaQwen 3.5 27B27BS16.1 tok/s
AlibabaQwen 3.6 27B27BS12.3 tok/s
AlibabaQwen 3.6 35B A3B35BA16.6 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BS38.5 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run Devstral Small 2 24B Instruct?

Yes, Intel Arc Pro B60 24GB can run Devstral Small 2 24B Instruct with a S grade (Tight fit). Expected decode speed: 18.1 tok/s.

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

Devstral Small 2 24B Instruct (24B parameters) requires approximately 20.4 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 Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Devstral Small 2 24B Instruct achieves approximately 18.1 tokens per second decode speed with a time-to-first-token of 10707ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on Intel Arc Pro B60 24GB receives a S grade with 18.1 tok/s and 40K context.

What context window can Devstral Small 2 24B Instruct use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Devstral Small 2 24B Instruct can safely use up to 40K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Devstral Small 2 24B Instruct feels slow on Intel Arc Pro B60 24GB?

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.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Devstral Small 2 24B Instruct?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc Pro B60 24GBSee all hardware for Devstral Small 2 24B Instruct
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