B100 192GBOpción económica192 GB VRAM (+4)8000 GB/s (+200)
C
Sube la velocidad estimada de decodificación alrededor de un 27%.46.9 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 27%.
~$35,000 MSRP
Can Baichuan M3 235B i1 run on H100 NVL 188GB?
YES — With Offload
C51Usable
Estimated from fit model
Baichuan M3 235B i1 needs ~190.6 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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) — 190.6 GB, 36.8 tok/s, Runs with offload (needs ~1.9 GB host RAM)
190.6 GB required188.0 GB available
101% VRAM needed
2.6 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1.9 GB host RAM)
Decode
36.8 tok/s
TTFT
5268 ms
Safe context
14K
Memory
190.6 GB / 188.0 GB
Memory breakdown
Weights143.4 GB
KV Cache27.5 GB
Runtime0.9 GB
Headroom18.8 GB
See how fast it feels
See how fast it feelsBaichuan M3 235B i1 on H100 NVL 188GB
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: 36.8 tok/s decode · 5.3s TTFT (warm) · 92 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 | 44.1 tok/s | 2396 ms | 14K |
| Coding | C | Runs with offload (needs ~1.9 GB host RAM) | 36.8 tok/s | 5268 ms | 14K |
| Agentic Coding | D | Very compromised (needs ~19.8 GB host RAM) | 29.4 tok/s | 9573 ms | 14K |
| Reasoning | C | Runs with offload (needs ~1.9 GB host RAM) | 36.8 tok/s | 6225 ms | 14K |
| RAG | D | Very compromised (needs ~19.8 GB host RAM) | 29.4 tok/s | 11966 ms | 14K |
Quantization options
How Baichuan M3 235B i1 (235B params) fits at each quantization level on H100 NVL 188GB (188.0 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 |
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 startOpciones de mejora
Hardware que ejecuta bien Baichuan M3 235B i1
Frequently asked questions
Can H100 NVL 188GB run Baichuan M3 235B i1?
Yes, H100 NVL 188GB can run Baichuan M3 235B i1 with a C grade (Runs with offload (needs ~1.9 GB host RAM)). Expected decode speed: 36.8 tok/s.
How much VRAM does Baichuan M3 235B i1 need?
Baichuan M3 235B i1 (235B parameters) requires approximately 190.6 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 H100 NVL 188GB?
On H100 NVL 188GB, Baichuan M3 235B i1 achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5268ms using Q4_K_M quantization.
Can H100 NVL 188GB run Baichuan M3 235B i1 for coding?
For coding workloads, Baichuan M3 235B i1 on H100 NVL 188GB receives a C grade with 36.8 tok/s and 14K context.
What context window can Baichuan M3 235B i1 use on H100 NVL 188GB?
On H100 NVL 188GB, Baichuan M3 235B i1 can safely use up to 14K 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 H100 NVL 188GB?
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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