Can Baichuan M2 32B Q4 K M run on NVIDIA V100 32GB?

YES — Tight Fit

C50Usable

Estimated from fit model

Baichuan M2 32B Q4 K M needs ~27.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 27.7 GB, 30.9 tok/s, Tight fit

27.7 GB required32.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

30.9 tok/s

TTFT

6267 ms

Safe context

34K

Memory

27.7 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsBaichuan M2 32B Q4 K M on NVIDIA V100 32GB
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: 30.9 tok/s decode · 6.3s TTFT (warm) · 77 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	30.9 tok/s	3418 ms	34K
Coding	C	Tight fit	30.9 tok/s	6267 ms	34K
Agentic Coding	C	Runs with offload	30.9 tok/s	9116 ms	34K
Reasoning	C	Tight fit	30.9 tok/s	7407 ms	34K
RAG	C	Runs with offload	30.9 tok/s	11395 ms	34K

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C47
Q3_K_S	3	15.7 GB	Low	C49
NVFP4	4

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

Upgrade options

Hardware that runs Baichuan M2 32B Q4 K M well

RTX A6000 48GBBudget pick

48 GB VRAM (+16)

Adds memory headroom for longer context windows and future model growth.29.9 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+444)

Raises estimated decode speed by about 87%.57.8 tok/s decode

Raises estimated decode speed by about 87%.

Adds memory headroom for longer context windows and future model growth.

~$4,999 MSRP

NVIDIA L40 48GBNVIDIA upgrade

48 GB VRAM (+16)

Adds memory headroom for longer context windows and future model growth.34.5 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$5,500 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run Baichuan M2 32B Q4 K M?

Yes, NVIDIA V100 32GB can run Baichuan M2 32B Q4 K M with a C grade (Tight fit). Expected decode speed: 30.9 tok/s.

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

Baichuan M2 32B Q4 K M (32B parameters) requires approximately 27.7 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 V100 32GB?

On NVIDIA V100 32GB, Baichuan M2 32B Q4 K M achieves approximately 30.9 tokens per second decode speed with a time-to-first-token of 6267ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Baichuan M2 32B Q4 K M for coding?

For coding workloads, Baichuan M2 32B Q4 K M on NVIDIA V100 32GB receives a C grade with 30.9 tok/s and 34K context.

What context window can Baichuan M2 32B Q4 K M use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Baichuan M2 32B Q4 K M can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Baichuan M2 32B Q4 K M

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