Can Devstral Small 1.1 run on Intel Arc Pro B60 24GB?

YES — Tight Fit

S88Excellent

Estimated from fit model

Devstral Small 1.1 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

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 1.1 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	18.1 tok/s	5840 ms	40K
Coding	S	Tight fit	18.1 tok/s	10707 ms	40K
Agentic Coding	S	Runs with offload	18.1 tok/s	15574 ms	40K
Reasoning	S	Tight fit	18.1 tok/s	12654 ms	40K
RAG	S	Runs with offload	18.1 tok/s	19468 ms	40K

Quantization options

How Devstral Small 1.1 (24B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S88
Q3_K_S	3	11.8 GB	Low	S90
NVFP4	4	13.4 GB	Medium	S90
Q4_K_M	4	14.6 GB	Medium	S89
Q5_K_MBest for your GPU	5	17.3 GB	High	S89
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 1.1 on your machine.

Run

lms load Devstral-Small-2507 && lms server start

Your hardware

More models your Intel Arc Pro B60 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	37.2 tok/s
Qwen 3.5 27B	27B	S	16.1 tok/s
Qwen 3.6 27B	27B	S	12.3 tok/s
Qwen 3.6 35B A3B	35B	A	16.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	38.5 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run Devstral Small 1.1?

Yes, Intel Arc Pro B60 24GB can run Devstral Small 1.1 with a S grade (Tight fit). Expected decode speed: 18.1 tok/s.

How much VRAM does Devstral Small 1.1 need?

Devstral Small 1.1 (24B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 1.1?

The recommended quantization for Devstral Small 1.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 1.1 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Devstral Small 1.1 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 1.1 for coding?

For coding workloads, Devstral Small 1.1 on Intel Arc Pro B60 24GB receives a S grade with 18.1 tok/s and 40K context.

What context window can Devstral Small 1.1 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Devstral Small 1.1 can safely use up to 40K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Devstral Small 1.1 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 1.1?

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 24GB See all hardware for Devstral Small 1.1

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