RX 7900 XT 20GBコスパ重視
20 GB VRAM (+4)800 GB/s (+240)
C
Raises estimated decode speed by about 91%.98.4 tok/sデコード
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can DeepSeek R1 0528 Qwen3 8B run on Intel Arc A770 16GB?
YES — Runs Great
C51Usable
Estimated from fit model
DeepSeek R1 0528 Qwen3 8B needs ~8.3 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 8.3 GB, 51.6 tok/s, Runs well
8.3 GB required16.0 GB available
52% VRAM used
Fit status
Runs well
Decode
51.6 tok/s
TTFT
3749 ms
Safe context
147K
Memory
8.3 GB / 16.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsDeepSeek R1 0528 Qwen3 8B on Intel Arc A770 16GB
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: 51.6 tok/s decode · 3.7s 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.
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 | 51.6 tok/s | 2045 ms | 147K |
| Coding | C | Runs well | 51.6 tok/s | 3749 ms | 147K |
| Agentic Coding | C | Runs well | 51.6 tok/s | 5453 ms | 147K |
| Reasoning | C | Runs well | 51.6 tok/s | 4431 ms | 147K |
| RAG | C | Runs well | 51.6 tok/s | 6817 ms | 147K |
Quantization options
How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C47 |
Q3_K_S | 3 | 3.9 GB | Low | C48 |
NVFP4 | 4 | 4.5 GB | Medium | C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C49 |
Q5_K_M | 5 | 5.8 GB | High | C50 |
Q6_K | 6 | 6.6 GB | High | C50 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C51 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run DeepSeek R1 0528 Qwen3 8B on your machine.
Run
lms load hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf && lms server startアップグレードオプション
DeepSeek R1 0528 Qwen3 8Bを快適に動かすハードウェア
RTX A4500 20GBコスパ最良20 GB VRAM (+4)640 GB/s (+80)
C
Raises estimated decode speed by about 98%.102.3 tok/sデコード
Raises estimated decode speed by about 98%.
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.
〜$2,000 MSRP
Frequently asked questions
Can Intel Arc A770 16GB run DeepSeek R1 0528 Qwen3 8B?
Yes, Intel Arc A770 16GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 51.6 tok/s.
How much VRAM does DeepSeek R1 0528 Qwen3 8B need?
DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek R1 0528 Qwen3 8B?
The recommended quantization for DeepSeek R1 0528 Qwen3 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek R1 0528 Qwen3 8B run at on Intel Arc A770 16GB?
On Intel Arc A770 16GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 51.6 tokens per second decode speed with a time-to-first-token of 3749ms using Q4_K_M quantization.
Can Intel Arc A770 16GB run DeepSeek R1 0528 Qwen3 8B for coding?
For coding workloads, DeepSeek R1 0528 Qwen3 8B on Intel Arc A770 16GB receives a C grade with 51.6 tok/s and 147K context.
What context window can DeepSeek R1 0528 Qwen3 8B use on Intel Arc A770 16GB?
On Intel Arc A770 16GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if DeepSeek R1 0528 Qwen3 8B feels slow on Intel Arc A770 16GB?
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.
Would CUDA be a better path than Intel Arc A770 16GB for DeepSeek R1 0528 Qwen3 8B?
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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