Can Baichuan M2 32B Q4 K M run on RTX 5090 32GB?

YES — Tight Fit

C52Usable

○Estimated from fit model

Baichuan M2 32B Q4 K M needs ~27.7 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~62 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, 61.5 tok/s, Tight fit

27.7 GB required32.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

61.5 tok/s

TTFT

3148 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 RTX 5090 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: 61.5 tok/s decode · 3.1s TTFT (warm) · 154 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	B	Runs well	61.5 tok/s	1717 ms	34K
Coding	C	Tight fit	61.5 tok/s	3148 ms	34K
Agentic Coding	C	Runs with offload	61.5 tok/s	4578 ms	34K
Reasoning	C	Tight fit	61.5 tok/s	3720 ms	34K
RAG	C	Runs with offload	61.5 tok/s	5723 ms	34K

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on RTX 5090 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

👁 NVIDIA
RTX PRO 5000 Blackwell 48GBBudget pick

48 GB VRAM (+16)

Adds memory headroom for longer context windows and future model growth.57.8 tok/s decode

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

~$4,999 MSRP

👁 NVIDIA
NVIDIA A100 40GBBest value

40 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.66.9 tok/s decode