Can Cerebras-GPT 13B run on Intel Arc Pro B60 24GB?

YES — Tight Fit

B66Good

○Estimated from fit model

Cerebras-GPT 13B needs ~22.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q5_K_M quantization, expect ~27 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: 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) — 22.7 GB, 26.8 tok/s, Tight fit

22.7 GB required24.0 GB available

95% VRAM used

Fit status

Tight fit

Decode

26.8 tok/s

TTFT

7215 ms

Safe context

18K

Memory

22.7 GB / 24.0 GB

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCerebras-GPT 13B on Intel Arc Pro B60 24GB
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: 26.8 tok/s decode · 7.2s TTFT (warm) · 67 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	B	Runs well	26.8 tok/s	3935 ms	18K
Coding	B	Tight fit	26.8 tok/s	7215 ms	18K
Agentic Coding	F	Too heavy	11.1 tok/s	25458 ms	18K
Reasoning	B	Tight fit	26.8 tok/s	8527 ms	18K
RAG	F	Too heavy	11.1 tok/s	31823 ms	18K

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B62
Q3_K_S	3	6.4 GB	Low	B62
NVFP4	4

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "cerebras/Cerebras-GPT-13B" \
 --hf-file "Cerebras-GPT-13B-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options

Hardware that runs Cerebras-GPT 13B well

Radeon AI PRO R9700 32GBBudget pick

32 GB VRAM (+8)640 GB/s (+184)

Raises estimated decode speed by about 53%.41.1 tok/s decode

Raises estimated decode speed by about 53%.

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

~$1,899 MSRP

👁 NVIDIA
RTX 5090 32GBBest value

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 388%.130.8 tok/s decode

Raises estimated decode speed by about 388%.