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URL: https://willitrunai.com/can-run/wizard-math-7b-on-arc-pro-a60-12gb

⇱ WizardMath 7B on Intel Arc Pro A60 12GB? YES

Can WizardMath 7B run on Intel Arc Pro A60 12GB?

YES — Runs Great

A75Great
○Estimated from fit model

WizardMath 7B needs ~8.3 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~47 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 8.3 GB, 47.4 tok/s, Runs well
8.3 GB required12.0 GB available
69% VRAM used

Fit status

Runs well

Decode

47.4 tok/s

TTFT

4087 ms

Safe context

4K

Memory

8.3 GB / 12.0 GB

Memory breakdown

Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsWizardMath 7B on Intel Arc Pro A60 12GB
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: 47.4 tok/s decode · 4.1s TTFT (warm) · 118 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
ChatARuns well47.4 tok/s2229 ms4K
CodingARuns well47.4 tok/s4087 ms4K
Agentic CodingATight fit47.4 tok/s5945 ms4K
ReasoningARuns well47.4 tok/s4830 ms4K
RAGATight fit47.4 tok/s7431 ms4K

Quantization options

How WizardMath 7B (7B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowB70
Q3_K_S
3
3.4 GB
LowA70
NVFP4
4
3.9 GB
MediumA71
Q4_K_M
4
4.3 GB
MediumA72
Q5_K_M
5
5.0 GB
HighA73
Q6_K
6
5.7 GB
HighA73
Q8_0Best for your GPU
8
7.5 GB
Very HighA72
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run WizardMath 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \ --hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \ -c 4096 -ngl 99

Your hardware

More models your Intel Arc Pro A60 12GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen 3.5 9B		9BS36.8 tok/s
👁 Alibaba
Qwen 3 14B		14BA14.9 tok/s
👁 Alibaba
Qwen 3 8B		8BS41.4 tok/s
👁 Microsoft
Phi-4-reasoning-plus 14B		14.7BA12 tok/s
👁 NVIDIA
Nemotron Nano 8B		8BS41.4 tok/s

Frequently asked questions

See all results for Intel Arc Pro A60 12GBSee all hardware for WizardMath 7B