DeepSeek Coder V2 16B needs ~15.6 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~131 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
Fit status
Runs with offload
Decode
118.0 tok/s
TTFT
1640 ms
Safe context
18K
Memory
15.6 GB / 16.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 131.1 tok/s | 805 ms | 18K |
| Coding | A | Runs with offload | 131.1 tok/s | 1476 ms | 18K |
| Agentic Coding | B | Very compromised | 69.6 tok/s | 4043 ms | 18K |
| Reasoning | A | Runs with offload | 131.1 tok/s | 1745 ms | 18K |
| RAG | B | Very compromised | 69.6 tok/s | 5054 ms | 18K |
How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 6.2 GB | Low | A79 |
Q3_K_S | 3 | 7.8 GB | Low | A80 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek Coder V2 16B on your machine.
Run
lms load DeepSeek-Coder-V2-Lite-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 21B | A | 56 tok/s | ||
| 22B | A |
Yes, RTX 4070 Ti Super 16GB can run DeepSeek Coder V2 16B with a A grade (Runs with offload). Expected decode speed: 131.1 tok/s.
DeepSeek Coder V2 16B (16B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Ti Super 16GB, DeepSeek Coder V2 16B achieves approximately 131.1 tokens per second decode speed with a time-to-first-token of 1476ms using Q4_K_M quantization.
For coding workloads, DeepSeek Coder V2 16B on RTX 4070 Ti Super 16GB receives a A grade with 131.1 tok/s and 18K context.
On RTX 4070 Ti Super 16GB, DeepSeek Coder V2 16B can safely use up to 18K tokens of context. The model's official context limit is 131K, 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-coder-v2-16b-on-rtx-4070-ti-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A80 |
Q4_K_M | 4 | 9.8 GB | Medium | A80 |
Q5_K_MBest for your GPU | 5 | 11.5 GB | High | A79 |
Q6_K | 6 | 13.1 GB | High | F0 |
Q8_0 | 8 | 17.1 GB | Very High | F0 |
F16 | 16 | 32.8 GB | Maximum | F0 |
| 16.4 tok/s |
| 19B | A | 29.2 tok/s |
| 20B | B | 22.9 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.