DeepSeek Coder V2 16B needs ~16.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~138 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 well
Decode
137.7 tok/s
TTFT
1406 ms
Safe context
52K
Memory
16.7 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 137.7 tok/s | 767 ms | 52K |
| Coding | S | Runs well | 137.7 tok/s | 1406 ms | 52K |
| Agentic Coding | A | Tight fit | 137.7 tok/s | 2045 ms | 52K |
| Reasoning | S | Runs well | 137.7 tok/s | 1662 ms | 52K |
| RAG | A | Tight fit | 137.7 tok/s | 2556 ms | 52K |
How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 6.2 GB | Low | A75 |
Q3_K_S | 3 | 7.8 GB | Low | A76 |
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 |
|---|---|---|---|---|
| 30.5B | S | 85.4 tok/s | ||
| 27B | S | 37 tok/s |
Yes, RTX PRO 4000 Blackwell 24GB can run DeepSeek Coder V2 16B with a S grade (Runs well). Expected decode speed: 137.7 tok/s.
DeepSeek Coder V2 16B (16B parameters) requires approximately 16.7 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 PRO 4000 Blackwell 24GB, DeepSeek Coder V2 16B achieves approximately 137.7 tokens per second decode speed with a time-to-first-token of 1406ms using Q4_K_M quantization.
For coding workloads, DeepSeek Coder V2 16B on RTX PRO 4000 Blackwell 24GB receives a S grade with 137.7 tok/s and 52K context.
On RTX PRO 4000 Blackwell 24GB, DeepSeek Coder V2 16B can safely use up to 52K 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-pro-4000-blackwell-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
9.0 GB |
| Medium |
| A77 |
Q4_K_M | 4 | 9.8 GB | Medium | A77 |
Q5_K_M | 5 | 11.5 GB | High | A78 |
Q6_K | 6 | 13.1 GB | High | A79 |
Q8_0Best for your GPU | 8 | 17.1 GB | Very High | A78 |
F16 | 16 | 32.8 GB | Maximum | F0 |
| 27B | S | 37.1 tok/s |
| 30B | S | 88.3 tok/s |
| 35B | A | 49.1 tok/s |