Can Codestral 2 25.08 run on Intel Arc Pro B60 24GB?

YES — Runs Great

S86Excellent

Estimated from fit model

Codestral 2 25.08 needs ~19.2 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 19.2 GB, 18.5 tok/s, Runs well

19.2 GB required24.0 GB available

80% VRAM used

Fit status

Runs well

Decode

18.5 tok/s

TTFT

10442 ms

Safe context

48K

Memory

19.2 GB / 24.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 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: 18.5 tok/s decode · 10.4s TTFT (warm) · 46 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	18.5 tok/s	5695 ms	48K
Coding	S	Runs well	18.5 tok/s	10442 ms	48K
Agentic Coding	A	Tight fit	18.5 tok/s	15188 ms	48K
Reasoning	S	Runs well	18.5 tok/s	12340 ms	48K
RAG	A	Tight fit	18.5 tok/s	18985 ms	48K

Inference speed

Codestral 2 25.08 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 2 25.08 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~96 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	96.2	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	52.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	41.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	41.7	Fits
RTX 3090 24GB	24 GB	Q4_K_M	38.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	33.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	18.6	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	18.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.6	Fits
RTX 4070 12GB	12 GB	Q4_K_M	6.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	A82
Q3_K_S	3	10.8 GB	Low	A84
NVFP4	4	12.3 GB	Medium	A85
Q4_K_M	4	13.4 GB	Medium	A84
Q5_K_M	5	15.8 GB	High	A84
Q6_KBest for your GPU	6	18.0 GB	High	A84
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 2 25.08 on your machine.

Run

lms load codestral-2508 && 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 Codestral 2 25.08?

Yes, Intel Arc Pro B60 24GB can run Codestral 2 25.08 with a S grade (Runs well). Expected decode speed: 18.5 tok/s.

How much VRAM does Codestral 2 25.08 need?

Codestral 2 25.08 (22B parameters) requires approximately 19.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 2 25.08?

The recommended quantization for Codestral 2 25.08 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 2 25.08 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Codestral 2 25.08 achieves approximately 18.5 tokens per second decode speed with a time-to-first-token of 10442ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on Intel Arc Pro B60 24GB receives a S grade with 18.5 tok/s and 48K context.

What context window can Codestral 2 25.08 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Codestral 2 25.08 can safely use up to 48K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 2 25.08 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 Codestral 2 25.08?

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 Codestral 2 25.08

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