Would CUDA be a better path than Gaudi 3 128GB for Baichuan M3 235B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Baichuan M3 235B run on Gaudi 3 128GB?

Q: What should I upgrade first if Baichuan M3 235B feels slow on Gaudi 3 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

YES — With Q2_K

C48Usable

Estimated from fit model

Baichuan M3 235B needs ~132.9 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q2_K quantization, expect ~17 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 Gaudi 3 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

6.5 tok/s

TTFT

29571 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 Gaudi 3 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: 6.5 tok/s decode · 29.6s TTFT (warm) · 16 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade 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	7.7 tok/s	13770 ms	4K
Coding	F	Too heavy	6.5 tok/s	29571 ms	4K
Agentic Coding	F	Too heavy	4.9 tok/s	57120 ms	4K
Reasoning	F	Too heavy	6.5 tok/s	34947 ms	4K
RAG	F	Too heavy	4.9 tok/s	71400 ms	4K

Quantization options

How Baichuan M3 235B (235B params) fits at each quantization level on Gaudi 3 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

Mac Studio M3 Ultra 256GBBest value

256 GB Unified (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.3.4 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+160)8000 GB/s (+4300)

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

Frequently asked questions

Can Gaudi 3 128GB run Baichuan M3 235B?

Yes, Gaudi 3 128GB can run Baichuan M3 235B at Q2_K quantization (Runs with offload (needs ~3.4 GB host RAM)). The recommended Q4_K_M requires 184.6 GB which exceeds available memory, but at Q2_K it needs only 132.9 GB. Expected decode speed: 17.0 tok/s.

How much VRAM does Baichuan M3 235B need?

Baichuan M3 235B (235B parameters) requires approximately 184.6 GB at Q4_K_M quantization. On Gaudi 3 128GB, it fits at Q2_K using 132.9 GB.

What is the best quantization for Baichuan M3 235B?

The recommended quantization is Q4_K_M, but on Gaudi 3 128GB the best fitting quantization is Q2_K, which uses 132.9 GB.

What speed will Baichuan M3 235B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Baichuan M3 235B achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11377ms using Q2_K quantization.

Can Gaudi 3 128GB run Baichuan M3 235B for coding?

For coding workloads, Baichuan M3 235B on Gaudi 3 128GB receives a F grade with 6.5 tok/s and 4K context.

What context window can Baichuan M3 235B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Baichuan M3 235B can safely use up to 13K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Gaudi 3 128GB See all hardware for Baichuan M3 235B

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