Can Baichuan M2 32B Q4 K M run on NVIDIA H100 80GB?

YES — Runs Great

C50Usable

Estimated from fit model

Baichuan M2 32B Q4 K M 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 M2 32B Q4 K M 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

Inference speed

Baichuan M2 32B Q4 K M inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Baichuan M2 32B Q4 K M 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 M2 32B Q4 K M (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	C45
F16Best for your GPU	16	65.6 GB	Maximum	C47

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

Can NVIDIA H100 80GB run Baichuan M2 32B Q4 K M?

Yes, NVIDIA H100 80GB can run Baichuan M2 32B Q4 K M with a C grade (Runs well). Expected decode speed: 144.2 tok/s.

How much VRAM does Baichuan M2 32B Q4 K M need?

Baichuan M2 32B Q4 K M (32B parameters) requires approximately 32.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan M2 32B Q4 K M?

The recommended quantization for Baichuan M2 32B Q4 K M is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan M2 32B Q4 K M run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Baichuan M2 32B Q4 K M achieves approximately 144.2 tokens per second decode speed with a time-to-first-token of 1343ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Baichuan M2 32B Q4 K M for coding?

For coding workloads, Baichuan M2 32B Q4 K M on NVIDIA H100 80GB receives a C grade with 144.2 tok/s and 219K context.

What context window can Baichuan M2 32B Q4 K M use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Baichuan M2 32B Q4 K M can safely use up to 219K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for Baichuan M2 32B Q4 K M

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