RTX 3060 12GBBest value12 GB VRAM (+2)
B
Makes the model fit on the accelerator instead of staying completely out of reach.16.6 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$329 MSRP
DeepSeek R1 Distill 14B needs ~12.0 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q3_K_S quantization, expect ~43 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
3.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
28.4 tok/s
TTFT
6824 ms
Safe context
4K
Memory
13.7 GB / 10.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 | F | Too heavy | 36.0 tok/s | 2932 ms | 4K |
| Coding | F | Too heavy | 28.4 tok/s | 6824 ms | 4K |
| Agentic Coding | F | Too heavy | 18.9 tok/s | 14937 ms | 4K |
| Reasoning | F | Too heavy | 28.4 tok/s | 8064 ms | 4K |
| RAG | F | Too heavy | 18.9 tok/s | 18672 ms | 4K |
How DeepSeek R1 Distill 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 | A77 |
Q3_K_SBest for your GPU | 3 | 6.9 GB | Low | A77 |
Copy-paste commands to run DeepSeek R1 Distill 14B on your machine.
Run
ollama run deepseek-r1Upgrade options
RTX 3060 12GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
~$329 MSRP
RTX 5060 Ti 16GBBudget 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.
~$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 14B at Q3_K_S quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q4_K_M requires 13.7 GB which exceeds available memory, but at Q3_K_S it needs only 12.0 GB. Expected decode speed: 43.3 tok/s.
DeepSeek R1 Distill 14B (14B parameters) requires approximately 13.7 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at Q3_K_S using 12.0 GB.
The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is Q3_K_S, which uses 12.0 GB.
On RTX 3080 10GB, DeepSeek R1 Distill 14B achieves approximately 43.3 tokens per second decode speed with a time-to-first-token of 4472ms using Q3_K_S quantization.
For coding workloads, DeepSeek R1 Distill 14B on RTX 3080 10GB receives a F grade with 28.4 tok/s and 4K context.
On RTX 3080 10GB, DeepSeek R1 Distill 14B can safely use up to 5K tokens of context at Q3_K_S quantization. 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-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.