Will It Run AI

Can baichuan inc Baichuan M2 32B run on AMD Instinct MI350X 288GB?

YES — Runs Great

C45Usable
Estimated from fit model

baichuan inc Baichuan M2 32B 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
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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 inc Baichuan M2 32B 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well299.2 tok/s353 ms1.0M
CodingCRuns well299.2 tok/s647 ms1.0M
Agentic CodingCRuns well299.2 tok/s941 ms1.0M
ReasoningCRuns well299.2 tok/s765 ms1.0M
RAGCRuns well299.2 tok/s1177 ms1.0M

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowD36
Q3_K_S
3
15.7 GB
LowD36
NVFP4
4
17.9 GB
MediumD36
Q4_K_M
4
19.5 GB
MediumD36
Q5_K_M
5
23.0 GB
HighD36
Q6_K
6
26.2 GB
HighD37
Q8_0
8
34.2 GB
Very HighD37
F16Best for your GPU
16
65.6 GB
MaximumD40

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

Can AMD Instinct MI350X 288GB run baichuan inc Baichuan M2 32B?

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

How much VRAM does baichuan inc Baichuan M2 32B need?

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 53.0 GB of memory with Q4_K_M quantization.

What is the best quantization for baichuan inc Baichuan M2 32B?

The recommended quantization for baichuan inc Baichuan M2 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will baichuan inc Baichuan M2 32B run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, baichuan inc Baichuan M2 32B 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 inc Baichuan M2 32B for coding?

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

What context window can baichuan inc Baichuan M2 32B use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, baichuan inc Baichuan M2 32B 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 288GBSee all hardware for baichuan inc Baichuan M2 32B
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