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URL: https://willitrunai.com/can-run/hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-h100-nvl-188gb

⇱ baichuan inc Baichuan M2 32B on H100 NVL 188GB? YES

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
Share:

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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well323.7 tok/s350 ms634K
CodingCRuns well323.7 tok/s598 ms634K
Agentic CodingCRuns well323.7 tok/s870 ms634K
ReasoningCRuns well323.7 tok/s707 ms634K
RAGCRuns well323.7 tok/s1088 ms634K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowD37
Q3_K_S
3
15.7 GB
LowD37
NVFP4
4
17.9 GB
MediumD37
Q4_K_M
4
19.5 GB
MediumD37
Q5_K_M
5
23.0 GB
HighD38
Q6_K
6
26.2 GB
HighD38
Q8_0
8
34.2 GB
Very HighD39
F16Best for your GPU
16
65.6 GB
MaximumC42

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 H100 NVL 188GBSee all hardware for baichuan inc Baichuan M2 32B