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

YES — Runs Great

B62Good

Estimated from fit model

Codestral 22B needs ~19.2 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~20 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, 19.7 tok/s, Runs well

19.2 GB required24.0 GB available

80% VRAM used

Fit status

Runs well

Decode

19.7 tok/s

TTFT

9815 ms

Safe context

33K

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 22B 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: 19.7 tok/s decode · 9.8s TTFT (warm) · 49 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	B	Runs well	19.7 tok/s	5354 ms	33K
Coding	B	Runs well	19.7 tok/s	9815 ms	33K
Agentic Coding	B	Tight fit	19.7 tok/s	14276 ms	33K
Reasoning	B	Runs well	19.7 tok/s	11600 ms	33K
RAG	B	Tight fit	19.7 tok/s	17845 ms	33K

Quantization options

How Codestral 22B (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	B58
Q3_K_S	3	10.8 GB	Low	B60
NVFP4	4	12.3 GB	Medium	B60
Q4_K_M	4	13.4 GB	Medium	B60
Q5_K_M	5	15.8 GB	High	B60
Q6_KBest for your GPU	6	18.0 GB	High	B59
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 on your machine.

Run

ollama run codestral

Opciones de mejora

Hardware que ejecuta bien Codestral 22B

RTX 5090 32GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 268%.72.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 268%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

No es solo un salto de hardware. También te deja en un ecosistema de runtimes más limpio para LLMs locales.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 191%.57.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 191%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

No es solo un salto de hardware. También te deja en un ecosistema de runtimes más limpio para LLMs locales.

~$2,499 MSRP

Frequently asked questions

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

Yes, Intel Arc Pro B60 24GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 19.7 tok/s.

How much VRAM does Codestral 22B need?

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

What is the best quantization for Codestral 22B?

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

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

On Intel Arc Pro B60 24GB, Codestral 22B achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9815ms using Q4_K_M quantization.

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

For coding workloads, Codestral 22B on Intel Arc Pro B60 24GB receives a B grade with 19.7 tok/s and 33K context.

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

On Intel Arc Pro B60 24GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B 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?

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

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