Can Baichuan M3 235B i1 run on AMD Instinct MI350X 288GB?

YES — Runs Great

C54Usable

Estimated from fit model

Baichuan M3 235B i1 needs ~200.6 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~41 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) — 200.6 GB, 40.7 tok/s, Runs well

200.6 GB required288.0 GB available

70% VRAM used

Fit status

Runs well

Decode

40.7 tok/s

TTFT

4752 ms

Safe context

67K

Memory

200.6 GB / 288.0 GB

Memory breakdown

Weights143.4 GB

KV Cache27.5 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsBaichuan M3 235B i1 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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 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	40.7 tok/s	2592 ms	67K
Coding	C	Runs well	40.7 tok/s	4752 ms	67K
Agentic Coding	C	Runs well	40.7 tok/s	6912 ms	67K
Reasoning	C	Runs well	40.7 tok/s	5616 ms	67K
RAG	C	Runs well	40.7 tok/s	8640 ms	67K

Inference speed

Baichuan M3 235B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Baichuan M3 235B i1 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~3 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	3.4	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	2.0	Too big
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	2.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Baichuan M3 235B i1 (235B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	91.7 GB	Low	C43
Q3_K_S	3	115.2 GB	Low	C44
NVFP4	4	131.6 GB	Medium	C45
Q4_K_M	4	143.4 GB	Medium	C46
Q5_K_M	5	169.2 GB	High	C47
Q6_KBest for your GPU	6	192.7 GB	High	C47
Q8_0	8	251.5 GB	Very High	F0
F16	16	481.7 GB	Maximum	F0

Get started

Copy-paste commands to run Baichuan M3 235B i1 on your machine.

Run

lms load hf-mradermacher--baichuan-m3-235b-i1-gguf && lms server start

Frequently asked questions

Can AMD Instinct MI350X 288GB run Baichuan M3 235B i1?

Yes, AMD Instinct MI350X 288GB can run Baichuan M3 235B i1 with a C grade (Runs well). Expected decode speed: 40.7 tok/s.

How much VRAM does Baichuan M3 235B i1 need?

Baichuan M3 235B i1 (235B parameters) requires approximately 200.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan M3 235B i1?

The recommended quantization for Baichuan M3 235B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan M3 235B i1 run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Baichuan M3 235B i1 achieves approximately 40.7 tokens per second decode speed with a time-to-first-token of 4752ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Baichuan M3 235B i1 for coding?

For coding workloads, Baichuan M3 235B i1 on AMD Instinct MI350X 288GB receives a C grade with 40.7 tok/s and 67K context.

What context window can Baichuan M3 235B i1 use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Baichuan M3 235B i1 can safely use up to 67K 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 M3 235B i1

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--baichuan-m3-235b-i1-gguf-on-instinct-mi350x-288gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

40.7 tok/s

2592 ms

67K

Coding

Runs well

40.7 tok/s

4752 ms

67K

Agentic Coding

Runs well

40.7 tok/s

6912 ms

67K

Reasoning

Runs well

40.7 tok/s

5616 ms

67K

RAG

Runs well

40.7 tok/s

8640 ms

67K

Baichuan M3 235B i1 inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

3.4

Heavy offload

RTX 5090 32GB

32 GB

Q4_K_M

2.0

Too big

RTX 4090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

2.0

Too big

RTX 3090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

2.0

Too big

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

2.0

Too big

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

2.0

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

2.0

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

2.0

Too big

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

2.0

Too big

4× RTX 3060 12GB

48 GB

Q4_K_M

2.0

Too big

Quantization options

How Baichuan M3 235B i1 (235B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	91.7 GB	Low	C43
Q3_K_S	3	115.2 GB	Low	C44
NVFP4	4	131.6 GB	Medium	C45
Q4_K_M	4	143.4 GB	Medium	C46
Q5_K_M	5	169.2 GB	High	C47
Q6_KBest for your GPU	6	192.7 GB	High	C47
Q8_0	8	251.5 GB	Very High	F0
F16	16	481.7 GB	Maximum	F0

Frequently asked questions

Can AMD Instinct MI350X 288GB run Baichuan M3 235B i1?

Yes, AMD Instinct MI350X 288GB can run Baichuan M3 235B i1 with a C grade (Runs well). Expected decode speed: 40.7 tok/s.

How much VRAM does Baichuan M3 235B i1 need?

Baichuan M3 235B i1 (235B parameters) requires approximately 200.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan M3 235B i1?

The recommended quantization for Baichuan M3 235B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan M3 235B i1 run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Baichuan M3 235B i1 achieves approximately 40.7 tokens per second decode speed with a time-to-first-token of 4752ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Baichuan M3 235B i1 for coding?

For coding workloads, Baichuan M3 235B i1 on AMD Instinct MI350X 288GB receives a C grade with 40.7 tok/s and 67K context.

What context window can Baichuan M3 235B i1 use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Baichuan M3 235B i1 can safely use up to 67K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.