Qwen 3 235B A22B needs ~155.1 GB but NVIDIA H100 80GB only has 80.0 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
75.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
16.4 tok/s
TTFT
11818 ms
Safe context
4K
Memory
155.1 GB / 80.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 155.1 GB, but this setup only exposes 80.0 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 | 15.2 tok/s | 6943 ms | 4K |
| Coding | F | Too heavy | 15.0 tok/s | 12926 ms | 4K |
| Agentic Coding | F | Too heavy | 14.5 tok/s | 19379 ms | 4K |
| Reasoning | F | Too heavy | 15.0 tok/s | 15276 ms | 4K |
| RAG | F | Too heavy | 14.5 tok/s | 24223 ms | 4K |
How Qwen 3 235B A22B (235B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 91.7 GB | Low | F0 |
Q3_K_S | 3 | 115.2 GB | Low | F0 |
NVFP4 | 4 |
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
NVIDIA H200 141GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 242%.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 191%.
~$30,000 MSRP
No, Qwen 3 235B A22B requires more memory than NVIDIA H100 80GB provides.
Qwen 3 235B A22B (235B parameters) requires approximately 155.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 235B A22B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA H100 80GB, Qwen 3 235B A22B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12926ms using Q4_K_M quantization.
For coding workloads, Qwen 3 235B A22B on NVIDIA H100 80GB receives a F grade with 15.0 tok/s and 4K context.
On NVIDIA H100 80GB, Qwen 3 235B A22B can safely use up to 4K 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/qwen-3-235b-a22b-on-h100-80gb" 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 | 143.4 GB | Medium | F0 |
Q5_K_M | 5 | 169.2 GB | High | F0 |
Q6_K | 6 | 192.7 GB | High | F0 |
Q8_0 | 8 | 251.5 GB | Very High | F0 |
F16 | 16 | 481.7 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.
