Would CUDA be a better path than Intel Arc B570 10GB for Mistral Small 3.1 24B?

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.

Can Mistral Small 3.1 24B run on Intel Arc B570 10GB?

Q: What should I upgrade first if Mistral Small 3.1 24B 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.

NO — Won't Fit

F0Won't run

Estimated from fit model

Mistral Small 3.1 24B needs ~19.0 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) — 19.0 GB, exceeds 10.0 GB available

19.0 GB required10.0 GB available

190% VRAM needed

9.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.1 tok/s

TTFT

62109 ms

Safe context

Memory

19.0 GB / 10.0 GB

Offload

50%

Memory breakdown

Weights14.6 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 feelsMistral Small 3.1 24B 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: 3.1 tok/s decode · 62.1s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 19.0 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	3.6 tok/s	29582 ms	4K
Coding	F	Too heavy	2.9 tok/s	66767 ms	4K
Agentic Coding	F	Too heavy	2.4 tok/s	115633 ms	4K
Reasoning	F	Too heavy	3.1 tok/s	73402 ms	4K
RAG	F	Too heavy	2.4 tok/s	144541 ms	4K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	F0
Q3_K_S	3	11.8 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Mistral Small 3.1 24B well

Intel Arc Pro B60 24GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.18.1 tok/s decode

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.

~$599 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+22)1792 GB/s (+1412)

Makes the model fit on the accelerator instead of staying completely out of reach.70.5 tok/s decode

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.

~$1,999 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+118)3200 GB/s (+2820)

Makes the model fit on the accelerator instead of staying completely out of reach.148 tok/s decode

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.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+118)3700 GB/s (+3320)

Makes the model fit on the accelerator instead of staying completely out of reach.190.2 tok/s decode

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.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run Mistral Small 3.1 24B?

No, Mistral Small 3.1 24B requires more memory than Intel Arc B570 10GB provides.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 19.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.1 24B?

The recommended quantization for Mistral Small 3.1 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.1 24B run at on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Mistral Small 3.1 24B achieves approximately 2.9 tokens per second decode speed with a time-to-first-token of 66767ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Mistral Small 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on Intel Arc B570 10GB receives a F grade with 2.9 tok/s and 4K context.

What context window can Mistral Small 3.1 24B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Mistral Small 3.1 24B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 3.1 24B feels slow on Intel Arc B570 10GB?

See all results for Intel Arc B570 10GB See all hardware for Mistral Small 3.1 24B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/mistral-small-3.1-24b-on-arc-b570-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: