Baichuan M3 235B i1 needs ~181.7 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
85.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.7 tok/s
TTFT
28773 ms
Safe context
4K
Memory
181.7 GB / 96.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 181.7 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 | 7.7 tok/s | 13781 ms | 4K |
| Coding | F | Too heavy | 6.7 tok/s | 28773 ms | 4K |
| Agentic Coding | F | Too heavy | 5.3 tok/s | 52825 ms | 4K |
| Reasoning | F | Too heavy | 6.7 tok/s | 34004 ms | 4K |
| RAG | F | Too heavy | 5.3 tok/s | 66032 ms | 4K |
How Baichuan M3 235B i1 (235B params) fits at each quantization level on NVIDIA H20 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 |
Upgrade options
NVIDIA B200 180GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 445%.
~$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, Baichuan M3 235B i1 requires more memory than NVIDIA H20 96GB provides.
Baichuan M3 235B i1 (235B parameters) requires approximately 181.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan M3 235B i1 is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA H20 96GB, Baichuan M3 235B i1 achieves approximately 6.7 tokens per second decode speed with a time-to-first-token of 28773ms using Q4_K_M quantization.
For coding workloads, Baichuan M3 235B i1 on NVIDIA H20 96GB receives a F grade with 6.7 tok/s and 4K context.
On NVIDIA H20 96GB, Baichuan M3 235B i1 can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-mradermacher--baichuan-m3-235b-i1-gguf-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:
| 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.
