Can Baichuan M3 235B i1 run on AMD Instinct MI300X 192GB?

YES — With Offload

C50Usable

○Estimated from fit model

Baichuan M3 235B i1 needs ~191.0 GB VRAM. AMD Instinct MI300X 192GB has 192.0 GB. With Q4_K_M quantization, expect ~29 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 191.0 GB, 28.8 tok/s, Runs with offload

191.0 GB required192.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

28.8 tok/s

TTFT

6713 ms

Safe context

17K

Memory

191.0 GB / 192.0 GB

Memory breakdown

Weights143.4 GB

KV Cache27.5 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsBaichuan M3 235B i1 on AMD Instinct MI300X 192GB
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: 28.8 tok/s decode · 6.7s TTFT (warm) · 72 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	C	Tight fit	28.8 tok/s	3662 ms	17K
Coding	C	Runs with offload	28.8 tok/s	6713 ms	17K
Agentic Coding	D	Very compromised (needs ~17.4 GB host RAM)	16.5 tok/s	17097 ms	17K
Reasoning	C	Runs with offload	28.8 tok/s	7934 ms	17K
RAG	D	Very compromised (needs ~17.4 GB host RAM)	16.5 tok/s	21371 ms