Can baichuan inc Baichuan M2 32B run on NVIDIA H100 80GB?

YES — Runs Great

C50Usable

○Estimated from fit model

baichuan inc Baichuan M2 32B needs ~32.5 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~144 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) — 32.5 GB, 144.2 tok/s, Runs well

32.5 GB required80.0 GB available

41% VRAM used

Fit status

Runs well

Decode

144.2 tok/s

TTFT

1343 ms

Safe context

219K

Memory

32.5 GB / 80.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B on NVIDIA H100 80GB
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: 144.2 tok/s decode · 1.3s TTFT (warm) · 360 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	144.2 tok/s	733 ms	219K
Coding	C	Runs well	144.2 tok/s	1343 ms	219K
Agentic Coding	C	Runs well	144.2 tok/s	1953 ms	219K
Reasoning	C	Runs well	144.2 tok/s	1587 ms	219K
RAG	C	Runs well	144.2 tok/s	2442 ms	219K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C40
Q3_K_S	3	15.7 GB	Low	C41
NVFP4	4	17.9 GB	Medium	C41
Q4_K_M	4	19.5 GB	Medium	C41
Q5_K_M	5	23.0 GB	High	C42
Q6_K	6	26.2 GB	High	C43
Q8_0	8	34.2 GB	Very High	C44
F16Best for your GPU	16	65.6 GB	Maximum	C47

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

See all results for NVIDIA H100 80GB See all hardware for baichuan inc Baichuan M2 32B

URL: https://willitrunai.com/can-run/hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-h100-80gb

⇱ baichuan inc Baichuan M2 32B on NVIDIA H100 80GB? YES