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URL: https://willitrunai.com/can-run/hf-unsloth--deepseek-r1-distill-qwen-1-5b-gguf-on-arc-pro-a60-12gb

⇱ DeepSeek R1 Distill Qwen 1.5B on Intel Arc Pro A60 12GB? YES

Can DeepSeek R1 Distill Qwen 1.5B run on Intel Arc Pro A60 12GB?

YES — Runs Great

C43Usable
○Estimated from fit model

DeepSeek R1 Distill Qwen 1.5B needs ~3.2 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:

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) — 3.2 GB, 21.0 tok/s, Runs well
3.2 GB required12.0 GB available
27% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

818K

Memory

3.2 GB / 12.0 GB

Memory breakdown

Weights0.9 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill Qwen 1.5B 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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
ChatCRuns well21.0 tok/s5029 ms719K
CodingCRuns well21.0 tok/s9219 ms818K
Agentic CodingCRuns well21.0 tok/s13410 ms818K
ReasoningCRuns well21.0 tok/s10895 ms818K
RAGCRuns well21.0 tok/s16762 ms818K

Quantization options

How DeepSeek R1 Distill Qwen 1.5B (1.5B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.6 GB
LowC47
Q3_K_S
3
0.7 GB
LowC47
NVFP4
4
0.8 GB
MediumC47
Q4_K_M
4
0.9 GB
MediumC47
Q5_K_M
5
1.1 GB
HighC47
Q6_K
6
1.2 GB
HighC47
Q8_0
8
1.6 GB
Very HighC48
F16Best for your GPU
16
3.1 GB
MaximumC50

Get started

Copy-paste commands to run DeepSeek R1 Distill Qwen 1.5B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-qwen-1-5b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 Distill Qwen 1.5B well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick
16 GB VRAM (+4)448 GB/s (+64)
C
Raises estimated decode speed by about 36%.28.5 tok/s decode

Raises estimated decode speed by about 36%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$449 MSRP

👁 NVIDIA
RTX 5070 Ti 16GBBest value
16 GB VRAM (+4)896 GB/s (+512)
C
Raises estimated decode speed by about 36%.28.5 tok/s decode

Raises estimated decode speed by about 36%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$749 MSRP

Frequently asked questions

See all results for Intel Arc Pro A60 12GBSee all hardware for DeepSeek R1 Distill Qwen 1.5B