DeepSeek R1 0528 Qwen3 8B needs ~7.5 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Operating mode
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
Fit status
Tight fit
Decode
22.5 tok/s
TTFT
8608 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 22.5 tok/s | 4695 ms | 24K |
| Coding | C | Tight fit | 22.5 tok/s | 8608 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 15.0 tok/s | 18755 ms | 24K |
| Reasoning | C | Tight fit | 22.5 tok/s | 10173 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 15.0 tok/s | 23444 ms | 24K |
How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on Intel Arc A550M 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 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Copy-paste commands to run DeepSeek R1 0528 Qwen3 8B on your machine.
Run
lms load hf-unsloth--deepseek-r1-0528-qwen3-8b-gguf && lms server startUpgrade-Optionen
Intel Arc B570 10GBGünstige WahlRaises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
ca. $219 MSRP
Intel Arc B580 12GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
ca. $249 MSRP
Intel Arc A770 16GBIntel-UpgradeRaises estimated decode speed by about 129%.
Adds memory headroom for longer context windows and future model growth.
ca. $349 MSRP
Yes, Intel Arc A550M 8GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Tight fit). Expected decode speed: 22.5 tok/s.
DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 0528 Qwen3 8B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A550M 8GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8608ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 0528 Qwen3 8B on Intel Arc A550M 8GB receives a C grade with 22.5 tok/s and 24K context.
On Intel Arc A550M 8GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
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.
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