DeepSeek Coder V2 16B needs ~19.1 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~275 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
275.4 tok/s
TTFT
703 ms
Safe context
131K
Memory
19.1 GB / 48.0 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 | 275.4 tok/s | 383 ms | 131K |
| Coding | A | Runs well | 275.4 tok/s | 703 ms | 131K |
| Agentic Coding | A | Runs well | 275.4 tok/s | 1022 ms | 131K |
| Reasoning | A | Runs well | 275.4 tok/s | 831 ms | 131K |
| RAG | A | Runs well | 275.4 tok/s | 1278 ms | 131K |
How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 6.2 GB | Low | A71 |
Q3_K_S | 3 | 7.8 GB | Low | A71 |
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 | 170.7 tok/s | ||
| 27B | S | 74 tok/s |
Yes, RTX PRO 5000 Blackwell 48GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 275.4 tok/s.
DeepSeek Coder V2 16B (16B parameters) requires approximately 19.1 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 5000 Blackwell 48GB, DeepSeek Coder V2 16B achieves approximately 275.4 tokens per second decode speed with a time-to-first-token of 703ms using Q4_K_M quantization.
For coding workloads, DeepSeek Coder V2 16B on RTX PRO 5000 Blackwell 48GB receives a A grade with 275.4 tok/s and 131K context.
On RTX PRO 5000 Blackwell 48GB, DeepSeek Coder V2 16B can safely use up to 131K 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-5000-blackwell-48gb" 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 |
| A71 |
Q4_K_M | 4 | 9.8 GB | Medium | A71 |
Q5_K_M | 5 | 11.5 GB | High | A72 |
Q6_K | 6 | 13.1 GB | High | A72 |
Q8_0 | 8 | 17.1 GB | Very High | A74 |
F16Best for your GPU | 16 | 32.8 GB | Maximum | A76 |
| 27B | S | 74.3 tok/s |
| 35B | S | 143.5 tok/s |
| 30B | S | 176.6 tok/s |