Can Baichuan 13B run on Gaudi 3 128GB?

YES — Runs Great

B64Good

Estimated from fit model

Baichuan 13B needs ~35.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q5_K_M quantization, expect ~182 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

Q5_K_M (High quality) — 35.3 GB, 182.0 tok/s, Runs well

35.3 GB required128.0 GB available

28% VRAM used

Fit status

Runs well

Decode

182.0 tok/s

TTFT

1064 ms

Safe context

Memory

35.3 GB / 128.0 GB

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsBaichuan 13B on Gaudi 3 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 182.0 tok/s decode · 1.1s TTFT (warm) · 455 tok/s prefill

What limits this setup

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

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	182.0 tok/s	580 ms	8K
Coding	B	Runs well	182.0 tok/s	1064 ms	8K
Agentic Coding	B	Runs well	182.0 tok/s	1547 ms	8K
Reasoning	B	Runs well	182.0 tok/s	1257 ms	8K
RAG	B	Runs well	182.0 tok/s	1934 ms	8K

Quantization options

How Baichuan 13B (13B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	C54
Q3_K_S	3	6.4 GB	Low	C54
NVFP4	4	7.3 GB	Medium	C54
Q4_K_M	4	7.9 GB	Medium	C54
Q5_K_M	5	9.4 GB	High	C54
Q6_K	6	10.7 GB	High	C54
Q8_0	8	13.9 GB	Very High	C55
F16Best for your GPU	16	26.7 GB	Maximum	B56

Get started

Copy-paste commands to run Baichuan 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "baichuan-inc/Baichuan-13B-Chat" \
  --hf-file "Baichuan-13B-Chat-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can Gaudi 3 128GB run Baichuan 13B?

Yes, Gaudi 3 128GB can run Baichuan 13B with a B grade (Runs well). Expected decode speed: 182.0 tok/s.

How much VRAM does Baichuan 13B need?

Baichuan 13B (13B parameters) requires approximately 35.3 GB of memory with Q5_K_M quantization.

What is the best quantization for Baichuan 13B?

The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.

What speed will Baichuan 13B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Baichuan 13B achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q5_K_M quantization.

Can Gaudi 3 128GB run Baichuan 13B for coding?

For coding workloads, Baichuan 13B on Gaudi 3 128GB receives a B grade with 182.0 tok/s and 8K context.

What context window can Baichuan 13B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Baichuan 13B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Baichuan 13B 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.

Would CUDA be a better path than Gaudi 3 128GB for Baichuan 13B?

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.

See all results for Gaudi 3 128GB See all hardware for Baichuan 13B

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