Can DeepSeek R1 0528 Qwen3 8B run on Intel Arc Pro A60 12GB?

YES — Runs Great

C53Usable

○Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~7.9 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 7.9 GB, 38.6 tok/s, Runs well

7.9 GB required12.0 GB available

66% VRAM used

Fit status

Runs well

Decode

38.6 tok/s

TTFT

5021 ms

Safe context

86K

Memory

7.9 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsDeepSeek R1 0528 Qwen3 8B 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: 38.6 tok/s decode · 5.0s TTFT (warm) · 96 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	C	Runs well	38.6 tok/s	2739 ms	86K
Coding	C	Runs well	38.6 tok/s	5021 ms	86K
Agentic Coding	C	Runs well	38.6 tok/s	7303 ms	86K
Reasoning	C	Runs well	38.6 tok/s	5934 ms	86K
RAG	C	Runs well	38.6 tok/s	9129 ms	86K

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C50
Q3_K_S	3	3.9 GB	Low	C51
NVFP4	4	4.5 GB	Medium	C51
Q4_K_M	4	4.9 GB	Medium	C52
Q5_K_M	5	5.8 GB	High	C52
Q6_K	6	6.6 GB	High	C52
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek R1 0528 Qwen3 8B on your machine.

Run

lms load hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf && lms server start

Frequently asked questions

See all results for Intel Arc Pro A60 12GB See all hardware for DeepSeek R1 0528 Qwen3 8B

URL: https://willitrunai.com/can-run/hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf-on-arc-pro-a60-12gb

⇱ DeepSeek R1 0528 Qwen3 8B on Intel Arc Pro A60 12GB? YES