Can Baichuan M2 32B Q4 K M run on AMD Instinct MI350X 288GB?

YES — Runs Great

C45Usable

Estimated from fit model

Baichuan M2 32B Q4 K M needs ~53.0 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~299 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) — 53.0 GB, 299.2 tok/s, Runs well

53.0 GB required288.0 GB available

18% VRAM used

Fit status

Runs well

Decode

299.2 tok/s

TTFT

647 ms

Safe context

1.0M

Memory

53.0 GB / 288.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsBaichuan M2 32B Q4 K M on AMD Instinct MI350X 288GB
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: 299.2 tok/s decode · 647ms TTFT (warm) · 748 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	299.2 tok/s	353 ms	1.0M
Coding	C	Runs well	299.2 tok/s	647 ms	1.0M
Agentic Coding	C	Runs well	299.2 tok/s	941 ms	1.0M
Reasoning	C	Runs well	299.2 tok/s	765 ms	1.0M
RAG	C	Runs well	299.2 tok/s	1177 ms	1.0M

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	D36
Q3_K_S	3	15.7 GB	Low	D36
NVFP4	4	17.9 GB	Medium	D36
Q4_K_M	4	19.5 GB	Medium	D36
Q5_K_M	5	23.0 GB	High	D37
Q6_K	6	26.2 GB	High	D37
Q8_0	8	34.2 GB	Very High	D37
F16Best for your GPU	16	65.6 GB	Maximum	D40

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

Can AMD Instinct MI350X 288GB run Baichuan M2 32B Q4 K M?

Yes, AMD Instinct MI350X 288GB can run Baichuan M2 32B Q4 K M with a C grade (Runs well). Expected decode speed: 299.2 tok/s.

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

Baichuan M2 32B Q4 K M (32B parameters) requires approximately 53.0 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 AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Baichuan M2 32B Q4 K M achieves approximately 299.2 tokens per second decode speed with a time-to-first-token of 647ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Baichuan M2 32B Q4 K M for coding?

For coding workloads, Baichuan M2 32B Q4 K M on AMD Instinct MI350X 288GB receives a C grade with 299.2 tok/s and 1.0M context.

What context window can Baichuan M2 32B Q4 K M use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Baichuan M2 32B Q4 K M can safely use up to 1.0M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for Baichuan M2 32B Q4 K M

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