Can baichuan inc Baichuan M2 32B run on H100 NVL 188GB?

YES — Runs Great

C46Usable

Estimated from fit model

baichuan inc Baichuan M2 32B needs ~43.3 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~324 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 43.3 GB, 323.7 tok/s, Runs well

43.3 GB required188.0 GB available

23% VRAM used

Fit status

Runs well

Decode

323.7 tok/s

TTFT

598 ms

Safe context

634K

Memory

43.3 GB / 188.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B 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: 323.7 tok/s decode · 598ms TTFT (warm) · 809 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	323.7 tok/s	350 ms	634K
Coding	C	Runs well	323.7 tok/s	598 ms	634K
Agentic Coding	C	Runs well	323.7 tok/s	870 ms	634K
Reasoning	C	Runs well	323.7 tok/s	707 ms	634K
RAG	C	Runs well	323.7 tok/s	1088 ms	634K

Inference speed

baichuan inc Baichuan M2 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for baichuan inc Baichuan M2 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~62 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?
RTX 5090 32GB	32 GB	Q4_K_M	61.5	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	30.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	28.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	23.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	23.2	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	22.5	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	21.4	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	19.8	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	19.4	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	8.4	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.9	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

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 inc Baichuan M2 32B (32B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	D37
Q3_K_S	3	15.7 GB	Low	D37
NVFP4	4	17.9 GB	Medium	D37
Q4_K_M	4	19.5 GB	Medium	D37
Q5_K_M	5	23.0 GB	High	D38
Q6_K	6	26.2 GB	High	D38
Q8_0	8	34.2 GB	Very High	D39
F16Best for your GPU	16	65.6 GB	Maximum	C42

Get started

Copy-paste commands to run baichuan inc Baichuan M2 32B on your machine.

Run

lms load hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf && lms server start

Frequently asked questions

Can H100 NVL 188GB run baichuan inc Baichuan M2 32B?

Yes, H100 NVL 188GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 323.7 tok/s.

How much VRAM does baichuan inc Baichuan M2 32B need?

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 43.3 GB of memory with Q4_K_M quantization.

What is the best quantization for baichuan inc Baichuan M2 32B?

The recommended quantization for baichuan inc Baichuan M2 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will baichuan inc Baichuan M2 32B run at on H100 NVL 188GB?

On H100 NVL 188GB, baichuan inc Baichuan M2 32B achieves approximately 323.7 tokens per second decode speed with a time-to-first-token of 598ms using Q4_K_M quantization.

Can H100 NVL 188GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on H100 NVL 188GB receives a C grade with 323.7 tok/s and 634K context.

What context window can baichuan inc Baichuan M2 32B use on H100 NVL 188GB?

On H100 NVL 188GB, baichuan inc Baichuan M2 32B can safely use up to 634K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for H100 NVL 188GB See all hardware for baichuan inc Baichuan M2 32B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-h100-nvl-188gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: