Can Baichuan M2 32B Q4 K M run on NVIDIA A40 48GB?

YES — Runs Great

C51Usable

○Estimated from fit model

Baichuan M2 32B Q4 K M needs ~29.3 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 29.3 GB, 27.8 tok/s, Runs well

29.3 GB required48.0 GB available

61% VRAM used

Fit status

Runs well

Decode

27.8 tok/s

TTFT

6961 ms

Safe context

96K

Memory

29.3 GB / 48.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsBaichuan M2 32B Q4 K M on NVIDIA A40 48GB
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: 27.8 tok/s decode · 7.0s TTFT (warm) · 70 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	27.8 tok/s	3797 ms	96K
Coding	C	Runs well	27.8 tok/s	6961 ms	96K
Agentic Coding	C	Runs well	27.8 tok/s	10125 ms	96K
Reasoning	C	Runs well	27.8 tok/s	8227 ms	96K
RAG	C	Runs well	27.8 tok/s	12657 ms	96K

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C43
Q3_K_S	3	15.7 GB	Low	C44
NVFP4	4	17.9 GB	Medium	C45
Q4_K_M	4	19.5 GB	Medium	C46
Q5_K_M	5	23.0 GB	High	C47
Q6_K	6	26.2 GB	High	C48
Q8_0Best for your GPU	8	34.2 GB	Very High	C47
F16	16	65.6 GB	Maximum	F0

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 NVIDIA A40 48GB See all hardware for Baichuan M2 32B Q4 K M

URL: https://willitrunai.com/can-run/hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf-on-a40-48gb

⇱ Baichuan M2 32B Q4 K M on NVIDIA A40 48GB? YES