DeepSeek R1 Distill Qwen 14B needs ~12.7 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~30 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
Runs well
Decode
29.5 tok/s
TTFT
6561 ms
Safe context
48K
Memory
12.7 GB / 16.0 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 29.5 tok/s | 3579 ms | 48K |
| Coding | C | Runs well | 29.5 tok/s | 6561 ms | 48K |
| Agentic Coding | C | Tight fit | 29.5 tok/s | 9543 ms | 48K |
| Reasoning | C | Runs well | 29.5 tok/s | 7754 ms | 48K |
| RAG | C | Tight fit | 29.5 tok/s | 11929 ms | 48K |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C49 |
Q3_K_S | 3 | 6.9 GB | Low | C51 |
NVFP4 | 4 | 7.8 GB | Medium | C52 |
Q4_K_M | 4 | 8.5 GB | Medium | C52 |
Q5_K_M | 5 | 10.1 GB | High | C51 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | C51 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
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 startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 91%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$899 MSRP
Sube la velocidad estimada de decodificación alrededor de un 174%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$999 MSRP
Yes, Intel Arc A770 16GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs well). Expected decode speed: 29.5 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 12.7 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill Qwen 14B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A770 16GB, DeepSeek R1 Distill Qwen 14B achieves approximately 29.5 tokens per second decode speed with a time-to-first-token of 6561ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on Intel Arc A770 16GB receives a C grade with 29.5 tok/s and 48K context.
On Intel Arc A770 16GB, DeepSeek R1 Distill Qwen 14B can safely use up to 48K 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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