RTX 5060 Ti 16GBBudget pick16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
DeepSeek R1 Distill Qwen 14B needs ~12.3 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~34 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
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
34.1 tok/s
TTFT
5680 ms
Safe context
13K
Memory
12.3 GB / 12.0 GB
This setup is broadly balanced for this model.
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.
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 | Runs with offload | 47.7 tok/s | 2213 ms | 13K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 34.1 tok/s | 5680 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 26.2 tok/s | 10757 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 34.1 tok/s | 6712 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 26.2 tok/s |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C53 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
NVFP4 | 4 |
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 startUpgrade options
RTX 5060 Ti 16GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBBest valueAdds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$625 MSRP
Yes, RTX 4070 Super 12GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 34.1 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 12.3 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 RTX 4070 Super 12GB, DeepSeek R1 Distill Qwen 14B achieves approximately 34.1 tokens per second decode speed with a time-to-first-token of 5680ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on RTX 4070 Super 12GB receives a C grade with 34.1 tok/s and 13K context.
On RTX 4070 Super 12GB, DeepSeek R1 Distill Qwen 14B can safely use up to 13K tokens of context. The model's official context limit is —, 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/hf-unsloth--deepseek-r1-distill-qwen-14b-gguf-on-rtx-4070-super-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 13446 ms |
| 13K |
7.8 GB |
| Medium |
| C52 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | C52 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.