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.

Capabilities:

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

Inference speed

Baichuan M3 235B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Baichuan M3 235B i1 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.

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 start

Opções de upgrade

Hardware que roda bem Baichuan M3 235B i1

B100 192GBOpção econômica

192 GB VRAM (+4)8000 GB/s (+200)

Raises estimated decode speed by about 27%.46.9 tok/s decodificação

Raises estimated decode speed by about 27%.

~$35,000 MSRP

NVIDIA GB200 192GBMelhor custo-benefício

192 GB VRAM (+4)8000 GB/s (+200)

Raises estimated decode speed by about 27%.46.9 tok/s decodificação

Raises estimated decode speed by about 27%.

~$60,000 MSRP

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.

See all results for H100 NVL 188GB See all hardware for Baichuan M3 235B i1

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