DeepSeek R1 Distill 14B needs ~13.6 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~16 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
1.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
16.4 tok/s
TTFT
11770 ms
Safe context
7K
Memory
13.6 GB / 12.0 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.
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 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.1 GB host RAM) | 20.9 tok/s | 5048 ms | 7K |
| Coding | B | Very compromised (needs ~1 GB host RAM) | 16.4 tok/s | 11770 ms | 7K |
| Agentic Coding | F | Too heavy | 10.9 tok/s | 25835 ms | 7K |
| Reasoning | B | Very compromised | 15.2 tok/s | 15023 ms | 7K |
| RAG | F | Too heavy | 10.9 tok/s | 32294 ms |
How DeepSeek R1 Distill 14B (14B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A77 |
Q3_K_S | 3 | 6.9 GB | Low | A76 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek R1 Distill 14B on your machine.
Run
ollama run deepseek-r1Upgrade options
RTX 5060 Ti 16GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 108%.
~$449 MSRP
RTX 4060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 70%.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 69%.
~$625 MSRP
Yes, RTX A2000 12GB can run DeepSeek R1 Distill 14B with a B grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 16.4 tok/s.
DeepSeek R1 Distill 14B (14B parameters) requires approximately 13.6 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 A2000 12GB, DeepSeek R1 Distill 14B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11770ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 14B on RTX A2000 12GB receives a B grade with 16.4 tok/s and 7K context.
On RTX A2000 12GB, DeepSeek R1 Distill 14B can safely use up to 7K tokens of context. 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-a2000-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 7K |
7.8 GB |
| Medium |
| A76 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | A76 |
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.