Would CUDA be a better path than Intel Arc B580 12GB for DeepSeek R1 Distill Qwen 14B?

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.

Can DeepSeek R1 Distill Qwen 14B run on Intel Arc B580 12GB?

Q: What should I upgrade first if DeepSeek R1 Distill Qwen 14B feels slow on Intel Arc B580 12GB?

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.

YES — With Offload

C49Usable

Estimated from fit model

DeepSeek R1 Distill Qwen 14B needs ~12.3 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 12.3 GB, 18.7 tok/s, Runs with offload (needs ~0.2 GB host RAM)

12.3 GB required12.0 GB available

103% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

18.7 tok/s

TTFT

10355 ms

Safe context

13K

Memory

12.3 GB / 12.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill Qwen 14B on Intel Arc B580 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: 18.7 tok/s decode · 10.4s TTFT (warm) · 47 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	25.6 tok/s	4120 ms	13K
Coding	C	Runs with offload	18.7 tok/s	10355 ms	13K
Agentic Coding	D	Very compromised	14.5 tok/s	19452 ms	13K
Reasoning	C	Runs with offload	18.7 tok/s	12238 ms	13K
RAG	D	Very compromised	14.5 tok/s	24315 ms	13K

Quantization options

How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C53
Q3_K_S	3	6.9 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill Qwen 14B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-qwen-14b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 Distill Qwen 14B well

Intel Arc A770 16GBBudget pick

16 GB VRAM (+4)560 GB/s (+104)

Raises estimated decode speed by about 58%.29.5 tok/s decode

Raises estimated decode speed by about 58%.

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

~$349 MSRP

Intel Arc Pro B50 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.14.2 tok/s decode

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

~$399 MSRP

Intel Arc Pro B60 24GBIntel upgrade

24 GB VRAM (+12)

Raises estimated decode speed by about 54%.28.8 tok/s decode

Raises estimated decode speed by about 54%.

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

~$599 MSRP

Frequently asked questions

Can Intel Arc B580 12GB run DeepSeek R1 Distill Qwen 14B?

Yes, Intel Arc B580 12GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs with offload). Expected decode speed: 18.7 tok/s.

How much VRAM does DeepSeek R1 Distill Qwen 14B need?

DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 12.3 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill Qwen 14B?

The recommended quantization for DeepSeek R1 Distill Qwen 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill Qwen 14B run at on Intel Arc B580 12GB?

On Intel Arc B580 12GB, DeepSeek R1 Distill Qwen 14B achieves approximately 18.7 tokens per second decode speed with a time-to-first-token of 10355ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run DeepSeek R1 Distill Qwen 14B for coding?

For coding workloads, DeepSeek R1 Distill Qwen 14B on Intel Arc B580 12GB receives a C grade with 18.7 tok/s and 13K context.

What context window can DeepSeek R1 Distill Qwen 14B use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, DeepSeek R1 Distill Qwen 14B can safely use up to 13K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc B580 12GB See all hardware for DeepSeek R1 Distill Qwen 14B

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