DeepSeek R1 Distill Qwen 14B needs ~11.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With NVFP4 quantization, expect ~42 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
2.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
32.4 tok/s
TTFT
5983 ms
Safe context
4K
Memory
12.4 GB / 10.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Very compromised (needs ~1.2 GB host RAM) | 37.4 tok/s | 2825 ms | 4K |
| Coding | F | Too heavy | 32.4 tok/s | 5983 ms | 4K |
| Agentic Coding | F | Too heavy | 24.9 tok/s | 11308 ms | 4K |
| Reasoning | F | Too heavy | 32.4 tok/s | 7070 ms | 4K |
| RAG | F | Too heavy | 24.9 tok/s | 14135 ms | 4K |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 6.9 GB | Low | C52 |
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 3060 12GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
RTX 5060 Ti 16GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$499 MSRP
Yes, RTX 3080 10GB can run DeepSeek R1 Distill Qwen 14B at NVFP4 quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q4_K_M requires 12.4 GB which exceeds available memory, but at NVFP4 it needs only 11.7 GB. Expected decode speed: 41.8 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 12.4 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at NVFP4 using 11.7 GB.
The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is NVFP4, which uses 11.7 GB.
On RTX 3080 10GB, DeepSeek R1 Distill Qwen 14B achieves approximately 41.8 tokens per second decode speed with a time-to-first-token of 4628ms using NVFP4 quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on RTX 3080 10GB receives a F grade with 32.4 tok/s and 4K context.
On RTX 3080 10GB, DeepSeek R1 Distill Qwen 14B can safely use up to 4K tokens of context at NVFP4 quantization. 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-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
7.8 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 8.5 GB | Medium | F0 |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.