Can Codestral 22B v0.1 run on Intel Arc Pro B60 24GB?

YES — Runs Great

C52Usable

Estimated from fit model

Codestral 22B v0.1 needs ~19.3 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~18 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) — 19.3 GB, 18.3 tok/s, Runs well

19.3 GB required24.0 GB available

80% VRAM used

Fit status

Runs well

Decode

18.3 tok/s

TTFT

10551 ms

Safe context

45K

Memory

19.3 GB / 24.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 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: 18.3 tok/s decode · 10.6s 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	C	Runs well	18.3 tok/s	5755 ms	45K
Coding	C	Runs well	18.3 tok/s	10551 ms	45K
Agentic Coding	C	Tight fit	18.3 tok/s	15347 ms	45K
Reasoning	C	Runs well	18.3 tok/s	12470 ms	45K
RAG	C	Tight fit	18.3 tok/s	19184 ms	45K

Quantization options

How Codestral 22B v0.1 (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	C48
Q3_K_S	3	10.8 GB	Low	C49
NVFP4	4	12.3 GB	Medium	C50
Q4_K_M	4	13.4 GB	Medium	C50
Q5_K_M	5	15.8 GB	High	C50
Q6_KBest for your GPU	6	18.0 GB	High	C49
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 22B v0.1 on your machine.

Run

lms load hf-bartowski--codestral-22b-v0-1-gguf && lms server start

Opções de upgrade

Hardware que roda bem Codestral 22B v0.1

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 318%.76.5 tok/s decodificação

Raises estimated decode speed by about 318%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+440)

Raises estimated decode speed by about 207%.56.1 tok/s decodificação

Raises estimated decode speed by about 207%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Codestral 22B v0.1?

Yes, Intel Arc Pro B60 24GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 18.3 tok/s.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 19.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Codestral 22B v0.1 achieves approximately 18.3 tokens per second decode speed with a time-to-first-token of 10551ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Intel Arc Pro B60 24GB receives a C grade with 18.3 tok/s and 45K context.

What context window can Codestral 22B v0.1 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Codestral 22B v0.1 can safely use up to 45K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B v0.1 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 Codestral 22B v0.1?

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 Codestral 22B v0.1

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