DeepSeek V4 Flash needs ~169.8 GB but NVIDIA H20 96GB only has 96.0 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
73.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
23.2 tok/s
TTFT
8333 ms
Safe context
4K
Memory
169.8 GB / 96.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 169.8 GB, but this setup only exposes 96.0 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 | 23.4 tok/s | 4517 ms | 4K |
| Coding | F | Too heavy | 23.2 tok/s | 8333 ms | 4K |
| Agentic Coding | F | Too heavy | 22.9 tok/s | 12276 ms | 4K |
| Reasoning | F | Too heavy | 23.2 tok/s | 9849 ms | 4K |
| RAG | F | Too heavy | 22.9 tok/s | 15345 ms | 4K |
How DeepSeek V4 Flash (284B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 110.8 GB | Low | F0 |
Q3_K_S | 3 | 139.2 GB | Low | F0 |
NVFP4 | 4 | 159.0 GB | Medium | F0 |
Q4_K_M | 4 | 173.2 GB | Medium | F0 |
Q5_K_M | 5 | 204.5 GB | High | F0 |
Q6_K | 6 | 232.9 GB | High | F0 |
Q8_0 | 8 | 303.9 GB | Very High | F0 |
F16 | 16 | 582.2 GB | Maximum | F0 |
Upgrade options
NVIDIA B200 180GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$30,000 MSRP
B100 192GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$35,000 MSRP
NVIDIA GB200 192GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$60,000 MSRP
No, DeepSeek V4 Flash requires more memory than NVIDIA H20 96GB provides.
DeepSeek V4 Flash (284B parameters) requires approximately 169.8 GB of memory with NVFP4 quantization.
The recommended quantization for DeepSeek V4 Flash is NVFP4, which balances quality and memory efficiency.
On NVIDIA H20 96GB, DeepSeek V4 Flash achieves approximately 23.2 tokens per second decode speed with a time-to-first-token of 8333ms using NVFP4 quantization.
For coding workloads, DeepSeek V4 Flash on NVIDIA H20 96GB receives a F grade with 23.2 tok/s and 4K context.
On NVIDIA H20 96GB, DeepSeek V4 Flash can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-v4-flash-on-h20-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
