AMD Instinct MI350X 288GBBudget pick
288 GB VRAM (+96)
C
Adds memory headroom for longer context windows and future model growth.40.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
Baichuan M3 235B needs ~191.3 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~47 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
Fit status
Runs with offload
Decode
46.9 tok/s
TTFT
4130 ms
Safe context
16K
Memory
191.3 GB / 192.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 46.9 tok/s | 2253 ms | 16K |
| Coding | C | Runs with offload | 46.9 tok/s | 4130 ms | 16K |
| Agentic Coding | C | Very compromised (needs ~17.6 GB host RAM) | 32.2 tok/s | 8739 ms | 16K |
| Reasoning | C | Runs with offload | 46.9 tok/s | 4881 ms | 16K |
| RAG | C | Very compromised (needs ~17.6 GB host RAM) | 32.2 tok/s | 10924 ms | 16K |
Inference speed
Estimated decode speed (tokens/sec) for Baichuan M3 235B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~3 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 3.4 | Heavy offload |
 RTX 5090 32GB | 32 GB | Q4_K_M | 2.0 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 2.0 | Too big |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 2.0 | Too big |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Baichuan M3 235B (235B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 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 | 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 |
Copy-paste commands to run Baichuan M3 235B on your machine.
Run
lms load hf-mradermacher--baichuan-m3-235b-gguf && lms server startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
Adds memory headroom for longer context windows and future model growth.
~$20,000 MSRP
Yes, NVIDIA GB200 192GB can run Baichuan M3 235B with a C grade (Runs with offload). Expected decode speed: 46.9 tok/s.
Baichuan M3 235B (235B parameters) requires approximately 191.3 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 GB200 192GB, Baichuan M3 235B achieves approximately 46.9 tokens per second decode speed with a time-to-first-token of 4130ms using Q4_K_M quantization.
For coding workloads, Baichuan M3 235B on NVIDIA GB200 192GB receives a C grade with 46.9 tok/s and 16K context.
On NVIDIA GB200 192GB, Baichuan M3 235B can safely use up to 16K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-gb200-192gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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