Will It Run AI

Can baichuan inc Baichuan M2 32B run on NVIDIA H20 96GB?

YES — Runs Great

C49Usable
Estimated from fit model

baichuan inc Baichuan M2 32B needs ~34.1 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~166 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) — 34.1 GB, 166.0 tok/s, Runs well
34.1 GB required96.0 GB available
36% VRAM used

Fit status

Runs well

Decode

166.0 tok/s

TTFT

1166 ms

Safe context

280K

Memory

34.1 GB / 96.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom9.6 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B on NVIDIA H20 96GB
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: 166.0 tok/s decode · 1.2s TTFT (warm) · 415 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 well166.0 tok/s636 ms280K
CodingCRuns well166.0 tok/s1166 ms280K
Agentic CodingCRuns well166.0 tok/s1697 ms280K
ReasoningCRuns well166.0 tok/s1378 ms280K
RAGCRuns well166.0 tok/s2121 ms280K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowD39
Q3_K_S
3
15.7 GB
LowD40
NVFP4
4
17.9 GB
MediumD40
Q4_K_M
4
19.5 GB
MediumC40
Q5_K_M
5
23.0 GB
HighC41
Q6_K
6
26.2 GB
HighC41
Q8_0
8
34.2 GB
Very HighC43
F16Best for your GPU
16
65.6 GB
MaximumC47

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 NVIDIA H20 96GB run baichuan inc Baichuan M2 32B?

Yes, NVIDIA H20 96GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 166.0 tok/s.

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

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 34.1 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 NVIDIA H20 96GB?

On NVIDIA H20 96GB, baichuan inc Baichuan M2 32B achieves approximately 166.0 tokens per second decode speed with a time-to-first-token of 1166ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on NVIDIA H20 96GB receives a C grade with 166.0 tok/s and 280K context.

What context window can baichuan inc Baichuan M2 32B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, baichuan inc Baichuan M2 32B can safely use up to 280K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GBSee all hardware for baichuan inc Baichuan M2 32B
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