Can Baichuan M2 32B Q4 K M run on AMD Instinct MI100 32GB?

YES — Tight Fit

C51Usable

○Estimated from fit model

Baichuan M2 32B Q4 K M needs ~27.4 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 27.4 GB, 40.9 tok/s, Tight fit

27.4 GB required32.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

40.9 tok/s

TTFT

4734 ms

Safe context

36K

Memory

27.4 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsBaichuan M2 32B Q4 K M on AMD Instinct MI100 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: 40.9 tok/s decode · 4.7s TTFT (warm) · 102 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	40.9 tok/s	2582 ms	36K
Coding	C	Tight fit	40.9 tok/s	4734 ms	36K
Agentic Coding	C	Runs with offload	40.9 tok/s	6887 ms	36K
Reasoning	C	Tight fit	40.9 tok/s	5595 ms	36K
RAG	C	Runs with offload	40.9 tok/s	8608 ms	36K