Can DeepSeek R1 Distill Llama 8B run on Intel Arc B570 10GB?

YES — Runs Great

C55Usable

○Estimated from fit model

DeepSeek R1 Distill Llama 8B needs ~7.7 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~42 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) — 7.7 GB, 42.0 tok/s, Runs well

7.7 GB required10.0 GB available

77% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4604 ms

Safe context

55K

Memory

7.7 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill Llama 8B on Intel Arc B570 10GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	42.0 tok/s	2511 ms	55K
Coding	C	Runs well	42.0 tok/s	4604 ms	55K
Agentic Coding	C	Tight fit	42.0 tok/s	6697 ms	55K
Reasoning	C	Runs well	42.0 tok/s	5441 ms	55K
RAG	C	Tight fit	42.0 tok/s	8371 ms	55K

Quantization options

How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C51
Q3_K_S	3	3.9 GB	Low	C53
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill Llama 8B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-llama-8b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 Distill Llama 8B well

👁 NVIDIA
RTX 5070 12GBBudget pick

12 GB VRAM (+2)672 GB/s (+292)

Raises estimated decode speed by about 107%.86.8 tok/s decode

Raises estimated decode speed by about 107%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

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

~$549 MSRP

👁 NVIDIA
RTX 4070 Super 12GBBest value

12 GB VRAM (+2)504 GB/s (+124)

Raises estimated decode speed by about 74%.73.2 tok/s decode

Raises estimated decode speed by about 74%.