10 GB VRAM (+2)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Can DeepSeek R1 Distill Llama 8B run on Intel Arc A580 8GB?
YES — Tight Fit
C52Usable
○Estimated from fit model
DeepSeek R1 Distill Llama 8B needs ~7.5 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~51 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 7.5 GB, 51.4 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
51.4 tok/s
TTFT
3766 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill Llama 8B on Intel Arc A580 8GB
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: 51.4 tok/s decode · 3.8s TTFT (warm) · 129 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 | Tight fit | 51.4 tok/s | 2054 ms | 24K |
| Coding | C | Tight fit | 51.4 tok/s | 3766 ms | 24K |
| Agentic Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 34.3 tok/s | 8205 ms | 24K |
| Reasoning | C | Tight fit | 51.4 tok/s | 4451 ms | 24K |
| RAG | C | Runs with offload (needs ~0.3 GB host RAM) | 34.3 tok/s | 10257 ms |
Quantization options
How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C54 |
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 startUpgrade options
Hardware that runs DeepSeek R1 Distill Llama 8B well
12 GB VRAM (+4)
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12 GB VRAM (+4)
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Adds memory headroom for longer context windows and future model growth.38.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP