Will It Run AI

Can Mistral Small 3.2 24B run on Intel Arc Pro B60 24GB?

YES — Tight Fit

A83Great
Estimated from fit model

Mistral Small 3.2 24B needs ~20.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
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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) — 20.7 GB, 18.1 tok/s, Tight fit
20.7 GB required24.0 GB available
86% VRAM used

Fit status

Tight fit

Decode

18.1 tok/s

TTFT

10707 ms

Safe context

38K

Memory

20.7 GB / 24.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B on Intel Arc Pro B60 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well18.1 tok/s5840 ms38K
CodingATight fit18.1 tok/s10707 ms38K
Agentic CodingARuns with offload18.1 tok/s15574 ms38K
ReasoningATight fit18.1 tok/s12654 ms38K
RAGARuns with offload18.1 tok/s19468 ms38K

Quantization options

How Mistral Small 3.2 24B (24B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowA83
Q3_K_S
3
11.8 GB
LowA84
NVFP4
4
13.4 GB
MediumA84
Q4_K_M
4
14.6 GB
MediumA84
Q5_K_MBest for your GPU
5
17.3 GB
HighA83
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

Get started

Copy-paste commands to run Mistral Small 3.2 24B on your machine.

Run

ollama run mistral-small3.2

Your hardware

More models your Intel Arc Pro B60 24GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BS37.2 tok/s
AlibabaQwen 3.5 27B27BS16.1 tok/s
AlibabaQwen 3.6 27B27BS16.2 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BS38.5 tok/s
AlibabaQwen 3.5 35B A3B35BA21.4 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run Mistral Small 3.2 24B?

Yes, Intel Arc Pro B60 24GB can run Mistral Small 3.2 24B with a A grade (Tight fit). Expected decode speed: 18.1 tok/s.

How much VRAM does Mistral Small 3.2 24B need?

Mistral Small 3.2 24B (24B parameters) requires approximately 20.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.2 24B?

The recommended quantization for Mistral Small 3.2 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.2 24B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Mistral Small 3.2 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 3.2 24B for coding?

For coding workloads, Mistral Small 3.2 24B on Intel Arc Pro B60 24GB receives a A grade with 18.1 tok/s and 38K context.

What context window can Mistral Small 3.2 24B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Mistral Small 3.2 24B can safely use up to 38K 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 3.2 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 3.2 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 24GBSee all hardware for Mistral Small 3.2 24B
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