Can baichuan inc Baichuan M2 32B run on AMD Instinct MI250 128GB?

YES — Runs Great

C48Usable

○Estimated from fit model

baichuan inc Baichuan M2 32B needs ~37.0 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 37.0 GB, 111.5 tok/s, Runs well

37.0 GB required128.0 GB available

29% VRAM used

Fit status

Runs well

Decode

111.5 tok/s

TTFT

1737 ms

Safe context

404K

Memory

37.0 GB / 128.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B on AMD Instinct MI250 128GB
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: 111.5 tok/s decode · 1.7s TTFT (warm) · 279 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	111.5 tok/s	947 ms	404K
Coding	C	Runs well	111.5 tok/s	1737 ms	404K
Agentic Coding	C	Runs well	111.5 tok/s	2526 ms	404K
Reasoning	C	Runs well	111.5 tok/s	2052 ms	404K
RAG	C	Runs well	111.5 tok/s	3158 ms	404K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	D38
Q3_K_S	3	15.7 GB	Low	D38
NVFP4	4	17.9 GB	Medium	D39
Q4_K_M	4	19.5 GB	Medium	D39
Q5_K_M	5	23.0 GB	High	D39
Q6_K	6	26.2 GB	High	D39
Q8_0	8	34.2 GB	Very High	C41
F16Best for your GPU	16	65.6 GB	Maximum	C46

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

See all results for AMD Instinct MI250 128GB See all hardware for baichuan inc Baichuan M2 32B

URL: https://willitrunai.com/can-run/hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-instinct-mi250-128gb

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