DeepSeek V3.2 needs ~429.0 GB but NVIDIA B200 180GB only has 180.0 GB. Try a smaller quantization or lighter model.
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
249.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
10.7 tok/s
TTFT
18138 ms
Safe context
4K
Memory
429.0 GB / 180.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 429.0 GB, but this setup only exposes 180.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 9.7 tok/s | 10866 ms | 4K |
| Coding | F | Too heavy | 10.7 tok/s | 18138 ms | 4K |
| Agentic Coding | F | Too heavy | 10.7 tok/s | 26430 ms | 4K |
| Reasoning | F | Too heavy | 10.7 tok/s | 21436 ms | 4K |
| RAG | F | Too heavy | 10.7 tok/s | 33038 ms | 4K |
How DeepSeek V3.2 (671B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 261.7 GB | Low | F0 |
Q3_K_S | 3 | 328.8 GB | Low | F0 |
NVFP4 | 4 |
No, DeepSeek V3.2 requires more memory than NVIDIA B200 180GB provides.
DeepSeek V3.2 (671B parameters) requires approximately 429.0 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek V3.2 is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA B200 180GB, DeepSeek V3.2 achieves approximately 10.7 tokens per second decode speed with a time-to-first-token of 18138ms using Q4_K_M quantization.
For coding workloads, DeepSeek V3.2 on NVIDIA B200 180GB receives a F grade with 10.7 tok/s and 4K context.
On NVIDIA B200 180GB, DeepSeek V3.2 can safely use up to 4K tokens of context. The model's official context limit is 128K, 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-v3.2-671b-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 409.3 GB | Medium | F0 |
Q5_K_M | 5 | 483.1 GB | High | F0 |
Q6_K | 6 | 550.2 GB | High | F0 |
Q8_0 | 8 | 718.0 GB | Very High | F0 |
F16 | 16 | 1375.6 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.