Qwen 3 235B A22B needs ~105.0 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q2_K quantization, expect ~65 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
60.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
25.1 tok/s
TTFT
7727 ms
Safe context
4K
Memory
156.7 GB / 96.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 7.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 25.4 tok/s | 4151 ms | 4K |
| Coding | F | Too heavy | 25.1 tok/s | 7727 ms | 4K |
| Agentic Coding | F | Too heavy | 24.3 tok/s | 11581 ms | 4K |
| Reasoning | F | Too heavy | 25.1 tok/s | 9132 ms | 4K |
| RAG | F | Too heavy | 24.3 tok/s | 14477 ms | 4K |
How Qwen 3 235B A22B (235B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 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 |
Copy-paste commands to run Qwen 3 235B A22B on your machine.
Run
lms load Qwen3-235B-A22B-Instruct-2507 && lms server startUpgrade 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 124%.
~$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 90%.
~$30,000 MSRP
Yes, NVIDIA GH200 96GB can run Qwen 3 235B A22B at Q2_K quantization (Very compromised (needs ~7.9 GB host RAM)). The recommended Q4_K_M requires 156.7 GB which exceeds available memory, but at Q2_K it needs only 105.0 GB. Expected decode speed: 64.5 tok/s.
Qwen 3 235B A22B (235B parameters) requires approximately 156.7 GB at Q4_K_M quantization. On NVIDIA GH200 96GB, it fits at Q2_K using 105.0 GB.
The recommended quantization is Q4_K_M, but on NVIDIA GH200 96GB the best fitting quantization is Q2_K, which uses 105.0 GB.
On NVIDIA GH200 96GB, Qwen 3 235B A22B achieves approximately 64.5 tokens per second decode speed with a time-to-first-token of 3003ms using Q2_K quantization.
For coding workloads, Qwen 3 235B A22B on NVIDIA GH200 96GB receives a F grade with 25.1 tok/s and 4K context.
On NVIDIA GH200 96GB, Qwen 3 235B A22B can safely use up to 4K tokens of context at Q2_K quantization. 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-gh200-96gb" 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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.