Can Cerebras-GPT 13B run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

B66Good

Estimated from fit model

Cerebras-GPT 13B needs ~30.7 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q5_K_M quantization, expect ~61 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 30.7 GB, 60.7 tok/s, Runs well

30.7 GB required69.1 GB available

44% VRAM used

Fit status

Runs well

Decode

60.7 tok/s

TTFT

3190 ms

Safe context

79K

Memory

30.7 GB / 69.1 GB

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsCerebras-GPT 13B on Mac Studio M3 Ultra 96GB
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: 60.7 tok/s decode · 3.2s TTFT (warm) · 152 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	60.7 tok/s	1740 ms	79K
Coding	B	Runs well	60.7 tok/s	3190 ms	79K
Agentic Coding	B	Runs well	60.7 tok/s	4640 ms	79K
Reasoning	B	Runs well	60.7 tok/s	3770 ms	79K
RAG	B	Runs well	60.7 tok/s	5800 ms	79K

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B56
Q3_K_S	3	6.4 GB	Low	B56
NVFP4	4	7.3 GB	Medium	B57
Q4_K_M	4	7.9 GB	Medium	B57
Q5_K_M	5	9.4 GB	High	B57
Q6_K	6	10.7 GB	High	B57
Q8_0	8	13.9 GB	Very High	B58
F16Best for your GPU	16	26.7 GB	Maximum	B60

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

Frequently asked questions

Can Mac Studio M3 Ultra 96GB run Cerebras-GPT 13B?

Yes, Mac Studio M3 Ultra 96GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 60.7 tok/s.

How much VRAM does Cerebras-GPT 13B need?

Cerebras-GPT 13B (13B parameters) requires approximately 30.7 GB of memory with Q5_K_M quantization.

What is the best quantization for Cerebras-GPT 13B?

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

What speed will Cerebras-GPT 13B run at on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, Cerebras-GPT 13B achieves approximately 60.7 tokens per second decode speed with a time-to-first-token of 3190ms using Q5_K_M quantization.

Can Mac Studio M3 Ultra 96GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on Mac Studio M3 Ultra 96GB receives a B grade with 60.7 tok/s and 79K context.

What context window can Cerebras-GPT 13B use on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, Cerebras-GPT 13B can safely use up to 79K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 96GB as fast as VRAM for Cerebras-GPT 13B?

Not always. Mac Studio M3 Ultra 96GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M3 Ultra 96GB See all hardware for Cerebras-GPT 13B

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