Can Mistral Small 24B run on Intel Arc Pro B60 24GB?

YES — Tight Fit

A81Great

Estimated from fit model

Mistral Small 24B needs ~20.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 20.4 GB, 18.1 tok/s, Tight fit

20.4 GB required24.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

18.1 tok/s

TTFT

10707 ms

Safe context

33K

Memory

20.4 GB / 24.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMistral Small 24B 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: 18.1 tok/s decode · 10.7s TTFT (warm) · 45 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	A	Runs well	18.1 tok/s	5840 ms	33K
Coding	A	Tight fit	18.1 tok/s	10707 ms	33K
Agentic Coding	A	Runs with offload	18.1 tok/s	15574 ms	33K
Reasoning	A	Tight fit	18.1 tok/s	12654 ms	33K
RAG	A	Runs with offload	18.1 tok/s	19468 ms	33K

Inference speed

Mistral Small 24B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mistral Small 24B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 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	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	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 Mistral Small 24B (24B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A81
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A82
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_MBest for your GPU	5	17.3 GB	High	A81
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run Mistral Small 24B on your machine.

Run

ollama run mistral-small

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 Mistral Small 24B?

Yes, Intel Arc Pro B60 24GB can run Mistral Small 24B with a A grade (Tight fit). Expected decode speed: 18.1 tok/s.

How much VRAM does Mistral Small 24B need?

Mistral Small 24B (24B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B?

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

What speed will Mistral Small 24B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Mistral Small 24B achieves approximately 18.1 tokens per second decode speed with a time-to-first-token of 10707ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Mistral Small 24B for coding?

For coding workloads, Mistral Small 24B on Intel Arc Pro B60 24GB receives a A grade with 18.1 tok/s and 33K context.

What context window can Mistral Small 24B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Mistral Small 24B 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 Mistral Small 24B 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 Mistral Small 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.

See all results for Intel Arc Pro B60 24GB See all hardware for Mistral Small 24B

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