Will It Run AI

Can baichuan inc Baichuan M2 32B run on AMD Instinct MI250X 128GB?

YES — Runs Great

C48Usable
Estimated from fit model

baichuan inc Baichuan M2 32B needs ~37.0 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With Q4_K_M quantization, expect ~128 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) — 37.0 GB, 127.9 tok/s, Runs well
37.0 GB required128.0 GB available
29% VRAM used

Fit status

Runs well

Decode

127.9 tok/s

TTFT

1514 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 MI250X 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: 127.9 tok/s decode · 1.5s TTFT (warm) · 320 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 well127.9 tok/s826 ms404K
CodingCRuns well127.9 tok/s1514 ms404K
Agentic CodingCRuns well127.9 tok/s2202 ms404K
ReasoningCRuns well127.9 tok/s1789 ms404K
RAGCRuns well127.9 tok/s2753 ms404K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowD38
Q3_K_S
3
15.7 GB
LowD38
NVFP4
4
17.9 GB
MediumD39
Q4_K_M
4
19.5 GB
MediumD39
Q5_K_M
5
23.0 GB
HighD39
Q6_K
6
26.2 GB
HighD39
Q8_0
8
34.2 GB
Very HighC41
F16Best for your GPU
16
65.6 GB
MaximumC46

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 MI250X 128GB run baichuan inc Baichuan M2 32B?

Yes, AMD Instinct MI250X 128GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 127.9 tok/s.

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

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 37.0 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 MI250X 128GB?

On AMD Instinct MI250X 128GB, baichuan inc Baichuan M2 32B achieves approximately 127.9 tokens per second decode speed with a time-to-first-token of 1514ms using Q4_K_M quantization.

Can AMD Instinct MI250X 128GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on AMD Instinct MI250X 128GB receives a C grade with 127.9 tok/s and 404K context.

What context window can baichuan inc Baichuan M2 32B use on AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, baichuan inc Baichuan M2 32B can safely use up to 404K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI250X 128GBSee all hardware for baichuan inc Baichuan M2 32B
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