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URL: https://willitrunai.com/can-run/hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf-on-b100-192gb

⇱ Baichuan M2 32B Q4 K M on B100 192GB? YES

Can Baichuan M2 32B Q4 K M run on B100 192GB?

YES — Runs Great

C46Usable
○Estimated from fit model

Baichuan M2 32B Q4 K M needs ~43.7 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~344 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) — 43.7 GB, 344.3 tok/s, Runs well
43.7 GB required192.0 GB available
23% VRAM used

Fit status

Runs well

Decode

344.3 tok/s

TTFT

562 ms

Safe context

649K

Memory

43.7 GB / 192.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom19.2 GB

See how fast it feels

See how fast it feelsBaichuan M2 32B Q4 K M on B100 192GB
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: 344.3 tok/s decode · 562ms TTFT (warm) · 861 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 well344.3 tok/s350 ms649K
CodingCRuns well344.3 tok/s562 ms649K
Agentic CodingCRuns well344.3 tok/s818 ms649K
ReasoningCRuns well344.3 tok/s665 ms649K
RAGCRuns well344.3 tok/s1022 ms649K

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on B100 192GB (192.0 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 M2 32B Q4 K M on your machine.

Run

lms load hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf && lms server start

Frequently asked questions

See all results for B100 192GBSee all hardware for Baichuan M2 32B Q4 K M