DeepSeek R1 Distill 14B needs ~13.5 GB but RTX 3060 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
5.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
9.6 tok/s
TTFT
20062 ms
Safe context
4K
Memory
13.5 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.5 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 12.3 tok/s | 8588 ms | 4K |
| Coding | F | Too heavy | 9.6 tok/s | 20062 ms | 4K |
| Agentic Coding | F | Too heavy | 6.4 tok/s | 44160 ms | 4K |
| Reasoning | F | Too heavy | 9.6 tok/s | 23710 ms | 4K |
| RAG | F | Too heavy | 6.4 tok/s | 55200 ms | 4K |
How DeepSeek R1 Distill 14B (14B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | F0 |
Q3_K_S | 3 | 6.9 GB | Low | F0 |
NVFP4 | 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 |
Opções de upgrade
RTX 3060 12GBMelhor custo-benefícioMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 73%.
~$329 MSRP
RTX 5060 Ti 16GBOpção econômicaMakes 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 16GBUpgrade NVIDIAMakes 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
No, DeepSeek R1 Distill 14B requires more memory than RTX 3060 Ti 8GB provides.
DeepSeek R1 Distill 14B (14B parameters) requires approximately 13.5 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 RTX 3060 Ti 8GB, DeepSeek R1 Distill 14B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20062ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 14B on RTX 3060 Ti 8GB receives a F grade with 9.6 tok/s and 4K context.
On RTX 3060 Ti 8GB, DeepSeek R1 Distill 14B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-14b-on-rtx-3060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
