DeepSeek R1 Distill Qwen 14B needs ~12.2 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~20 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
1.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.8 GB host RAM)
Decode
19.5 tok/s
TTFT
9929 ms
Safe context
4K
Memory
12.2 GB / 11.0 GB
Offload
10%
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.3 GB host RAM) | 22.7 tok/s | 4647 ms | 4K |
| Coding | D | Very compromised | 19.5 tok/s | 9929 ms | 4K |
| Agentic Coding | F | Too heavy | 14.8 tok/s | 19062 ms | 4K |
| Reasoning | D | Very compromised (needs ~0.8 GB host RAM) | 19.5 tok/s | 11735 ms | 4K |
| RAG | F | Too heavy | 14.8 tok/s | 23827 ms |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C53 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
NVFP4Best for your GPU |
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 pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 62%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 32%.
~$499 MSRP
Yes, GTX 1080 Ti 11GB can run DeepSeek R1 Distill Qwen 14B with a D grade (Very compromised). Expected decode speed: 19.5 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 12.2 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 GTX 1080 Ti 11GB, DeepSeek R1 Distill Qwen 14B achieves approximately 19.5 tokens per second decode speed with a time-to-first-token of 9929ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on GTX 1080 Ti 11GB receives a D grade with 19.5 tok/s and 4K context.
On GTX 1080 Ti 11GB, DeepSeek R1 Distill Qwen 14B can safely use up to 4K 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-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
7.8 GB |
| Medium |
| C52 |
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.