Baichuan M3 235B needs ~189.8 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~37 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
9.8 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~7.4 GB host RAM)
Decode
36.6 tok/s
TTFT
5284 ms
Safe context
10K
Memory
189.8 GB / 180.0 GB
Offload
10%
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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 46.9 tok/s | 2253 ms | 10K |
| Coding | C | Runs with offload | 36.6 tok/s | 5284 ms | 10K |
| Agentic Coding | F | Too heavy | 29.3 tok/s | 9611 ms | 10K |
| Reasoning | C | Runs with offload (needs ~7.4 GB host RAM) | 36.6 tok/s | 6245 ms | 10K |
| RAG | F | Too heavy | 29.3 tok/s | 12014 ms | 10K |
How Baichuan M3 235B (235B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 91.7 GB | Low | C47 |
Q3_K_S | 3 | 115.2 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run Baichuan M3 235B on your machine.
Run
lms load hf-mradermacher--baichuan-m3-235b-gguf && lms server startUpgrade options
B100 192GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 28%.
~$35,000 MSRP
NVIDIA GB200 192GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 28%.
~$60,000 MSRP
H100 NVL 188GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
~$60,000 MSRP
Yes, NVIDIA B200 180GB can run Baichuan M3 235B with a C grade (Runs with offload). Expected decode speed: 36.6 tok/s.
Baichuan M3 235B (235B parameters) requires approximately 189.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan M3 235B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA B200 180GB, Baichuan M3 235B achieves approximately 36.6 tokens per second decode speed with a time-to-first-token of 5284ms using Q4_K_M quantization.
For coding workloads, Baichuan M3 235B on NVIDIA B200 180GB receives a C grade with 36.6 tok/s and 10K context.
On NVIDIA B200 180GB, Baichuan M3 235B can safely use up to 10K 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-gguf-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:
131.6 GB |
| Medium |
| C47 |
Q4_K_MBest for your GPU | 4 | 143.4 GB | Medium | C47 |
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.