AMD Instinct MI350X 288GBコスパ重視
288 GB VRAM (+96)
C
Adds memory headroom for longer context windows and future model growth.40.7 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$8,000 MSRP
Can Baichuan M3 235B i1 run on NVIDIA GB200 192GB?
YES — With Offload
C51Usable
Estimated from fit model
Baichuan M3 235B i1 needs ~191.3 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~47 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 191.3 GB, 46.9 tok/s, Runs with offload
191.3 GB required192.0 GB available
100% VRAM used
Fit status
Runs with offload
Decode
46.9 tok/s
TTFT
4130 ms
Safe context
16K
Memory
191.3 GB / 192.0 GB
Memory breakdown
Weights143.4 GB
KV Cache27.5 GB
Runtime1.2 GB
Headroom19.2 GB
See how fast it feels
See how fast it feelsBaichuan M3 235B i1 on NVIDIA GB200 192GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 46.9 tok/s decode · 4.1s TTFT (warm) · 117 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| 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 |
Quantization options
How Baichuan M3 235B i1 (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 | C46 |
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 |
Get started
Copy-paste commands to run Baichuan M3 235B i1 on your machine.
Run
lms load hf-mradermacher--baichuan-m3-235b-i1-gguf && lms server startアップグレードオプション
Baichuan M3 235B i1を快適に動かすハードウェア
Frequently asked questions
Can NVIDIA GB200 192GB run Baichuan M3 235B i1?
Yes, NVIDIA GB200 192GB can run Baichuan M3 235B i1 with a C grade (Runs with offload). Expected decode speed: 46.9 tok/s.
How much VRAM does Baichuan M3 235B i1 need?
Baichuan M3 235B i1 (235B parameters) requires approximately 191.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Baichuan M3 235B i1?
The recommended quantization for Baichuan M3 235B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Baichuan M3 235B i1 run at on NVIDIA GB200 192GB?
On NVIDIA GB200 192GB, Baichuan M3 235B i1 achieves approximately 46.9 tokens per second decode speed with a time-to-first-token of 4130ms using Q4_K_M quantization.
Can NVIDIA GB200 192GB run Baichuan M3 235B i1 for coding?
For coding workloads, Baichuan M3 235B i1 on NVIDIA GB200 192GB receives a C grade with 46.9 tok/s and 16K context.
What context window can Baichuan M3 235B i1 use on NVIDIA GB200 192GB?
On NVIDIA GB200 192GB, Baichuan M3 235B i1 can safely use up to 16K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Baichuan M3 235B i1 feels slow on NVIDIA GB200 192GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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