Can Baichuan M3 235B run on AMD Instinct MI250X 128GB?

YES — With Q2_K

C48Usable

○Estimated from fit model

Baichuan M3 235B needs ~132.9 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With Q2_K quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Baichuan M3 235B at Q4_K_M needs 184.6 GB — too much for AMD Instinct MI250X 128GB (128.0 GB). Runs at Q2_K (132.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 184.6 GB, exceeds 128.0 GB available

184.6 GB required128.0 GB available

144% VRAM needed

56.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.0 tok/s

TTFT

32039 ms

Safe context

Memory

184.6 GB / 128.0 GB

Offload

30%

Memory breakdown

Weights143.4 GB

KV Cache27.5 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsBaichuan M3 235B 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: 6.0 tok/s decode · 32.0s TTFT (warm) · 15 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.1 tok/s	14845 ms	4K
Coding	F	Too heavy	6.0 tok/s	32039 ms	4K
Agentic Coding	F	Too heavy	4.5 tok/s	62451 ms	4K
Reasoning	F	Too heavy	6.0 tok/s	37865 ms	4K
RAG	F	Too heavy	4.5 tok/s	78063 ms	4K

Quantization options

How Baichuan M3 235B (235B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	91.7 GB	Low	C47
Q3_K_S	3	115.2 GB	Low	F0

Get started

Copy-paste commands to run Baichuan M3 235B on your machine.

Run

lms load hf-mradermacher--baichuan-m3-235b-gguf && lms server start

Upgrade options

Hardware that runs Baichuan M3 235B well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+160)8000 GB/s (+4800)

Makes the model fit on the accelerator instead of staying completely out of reach.40.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$8,000 MSRP

AMD Instinct MI300X 192GBBest value

192 GB VRAM (+64)5300 GB/s (+2100)

Makes the model fit on the accelerator instead of staying completely out of reach.28.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

AMD Instinct MI325X 256GBAMD upgrade

256 GB VRAM (+128)6000 GB/s (+2800)

Makes the model fit on the accelerator instead of staying completely out of reach.30.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$20,000 MSRP

Frequently asked questions

See all results for AMD Instinct MI250X 128GB See all hardware for Baichuan M3 235B

URL: https://willitrunai.com/can-run/hf-mradermacher--baichuan-m3-235b-gguf-on-instinct-mi250x-128gb

⇱

Can Baichuan M3 235B run on AMD Instinct MI250X 128GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Baichuan M3 235B well

Frequently asked questions