DeepSeek R1 Distill 14B needs ~14.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~31 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
31.1 tok/s
TTFT
6217 ms
Safe context
33K
Memory
14.8 GB / 24.0 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 | A | Runs well | 31.1 tok/s | 3391 ms | 33K |
| Coding | A | Runs well | 31.1 tok/s | 6217 ms | 33K |
| Agentic Coding | A | Runs well | 31.1 tok/s | 9043 ms | 33K |
| Reasoning | A | Runs well | 31.1 tok/s | 7347 ms | 33K |
| RAG | A | Runs well | 31.1 tok/s | 11304 ms | 33K |
How DeepSeek R1 Distill 14B (14B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A70 |
Q3_K_S | 3 | 6.9 GB | Low | A71 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek R1 Distill 14B on your machine.
Run
ollama run deepseek-r1Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 37.2 tok/s | |
| Qwen 3.5 27B | 27B | S | 16.1 tok/s |
Yes, Intel Arc Pro B60 24GB can run DeepSeek R1 Distill 14B with a A grade (Runs well). Expected decode speed: 31.1 tok/s.
DeepSeek R1 Distill 14B (14B parameters) requires approximately 14.8 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill 14B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, DeepSeek R1 Distill 14B achieves approximately 31.1 tokens per second decode speed with a time-to-first-token of 6217ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 14B on Intel Arc Pro B60 24GB receives a A grade with 31.1 tok/s and 33K context.
On Intel Arc Pro B60 24GB, DeepSeek R1 Distill 14B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-14b-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
7.8 GB |
| Medium |
| A72 |
Q4_K_M | 4 | 8.5 GB | Medium | A72 |
Q5_K_M | 5 | 10.1 GB | High | A73 |
Q6_K | 6 | 11.5 GB | High | A74 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | A74 |
F16 | 16 | 28.7 GB | Maximum | F0 |
| Qwen 3.6 27B | 27B | S | 12.3 tok/s |
| Qwen 3.6 35B A3B | 35B | A | 16.6 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 38.5 tok/s |
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.