Can Codestral 22B run on Intel Arc B570 10GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Codestral 22B needs ~17.8 GB but Intel Arc B570 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 17.8 GB, exceeds 10.0 GB available

17.8 GB required10.0 GB available

178% VRAM needed

7.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.9 tok/s

TTFT

49718 ms

Safe context

Memory

17.8 GB / 10.0 GB

Offload

40%

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral 22B on Intel Arc B570 10GB
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: 3.9 tok/s decode · 49.7s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 17.8 GB, but this setup only exposes 10.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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	F	Too heavy	4.5 tok/s	23452 ms	4K
Coding	F	Too heavy	3.9 tok/s	49718 ms	4K
Agentic Coding	F	Too heavy	3.0 tok/s	94047 ms	4K
Reasoning	F	Too heavy	3.9 tok/s	58757 ms	4K
RAG	F	Too heavy	3.0 tok/s	117559 ms	4K

Inference speed

Codestral 22B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 22B 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
RTX 4090 24GB	24 GB	Q4_K_M	61.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	55.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	52.5	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	44.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	37.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	37.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	37.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	35.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	26.5	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	19.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	17.6	Fits
RTX 4070 12GB	12 GB	Q4_K_M	9.4	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.9	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.4	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 22B (22B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	F0
Q3_K_S	3	10.8 GB	Low	F0
NVFP4	4	12.3 GB	Medium	F0
Q4_K_M	4	13.4 GB	Medium	F0
Q5_K_M	5	15.8 GB	High	F0
Q6_K	6	18.0 GB	High	F0
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

Upgrade-Optionen

Hardware, die Codestral 22B gut ausführt

Intel Arc A770 16GBBestes Preis-Leistungs-Verhältnis

16 GB VRAM (+6)560 GB/s (+180)

Makes the model fit on the accelerator instead of staying completely out of reach.11.3 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 190%.

ca. $349 MSRP

Intel Arc Pro B50 16GBIntel-Upgrade

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.5.6 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 44%.

ca. $399 MSRP

Intel Arc Pro B60 24GBGünstige Wahl

24 GB VRAM (+14)456 GB/s (+76)

Makes the model fit on the accelerator instead of staying completely out of reach.19.7 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $599 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run Codestral 22B?

No, Codestral 22B requires more memory than Intel Arc B570 10GB provides.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 17.8 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 B570 10GB?

On Intel Arc B570 10GB, Codestral 22B achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49718ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on Intel Arc B570 10GB receives a F grade with 3.9 tok/s and 4K context.

What context window can Codestral 22B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Codestral 22B can safely use up to 4K 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 B570 10GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Would CUDA be a better path than Intel Arc B570 10GB 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 B570 10GB See all hardware for Codestral 22B

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