Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Cerebras-GPT 13B?

Not always. Mac Studio M2 Ultra 128GB 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.

Can Cerebras-GPT 13B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

B64Good

Estimated from fit model

Cerebras-GPT 13B needs ~34.1 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q5_K_M quantization, expect ~51 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) — 34.1 GB, 50.6 tok/s, Runs well

34.1 GB required92.2 GB available

37% VRAM used

Fit status

Runs well

Decode

50.6 tok/s

TTFT

3829 ms

Safe context

111K

Memory

34.1 GB / 92.2 GB

Memory breakdown

Weights9.4 GB

KV Cache9.8 GB

Runtime1.2 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsCerebras-GPT 13B on Mac Studio M2 Ultra 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: 50.6 tok/s decode · 3.8s TTFT (warm) · 126 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	50.6 tok/s	2088 ms	111K
Coding	B	Runs well	50.6 tok/s	3829 ms	111K
Agentic Coding	B	Runs well	50.6 tok/s	5569 ms	111K
Reasoning	B	Runs well	50.6 tok/s	4525 ms	111K
RAG	B	Runs well	50.6 tok/s	6961 ms	111K

Inference speed

Cerebras-GPT 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Cerebras-GPT 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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?
RTX 5090 32GB	32 GB	Q5_K_M	130.8	Fits
RTX 4090 24GB	24 GB	Q5_K_M	83.5	Tight

Quantization options

How Cerebras-GPT 13B (13B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B55
Q3_K_S	3	6.4 GB	Low	B55
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

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

1792 GB/s (+992)

Raises estimated decode speed by about 224%.164 tok/s decode

Raises estimated decode speed by about 224%.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

1597 GB/s (+797)

Raises estimated decode speed by about 189%.146.2 tok/s decode

Raises estimated decode speed by about 189%.

~$9,999 MSRP

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Cerebras-GPT 13B?

Yes, Mac Studio M2 Ultra 128GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 50.6 tok/s.

How much VRAM does Cerebras-GPT 13B need?

Cerebras-GPT 13B (13B parameters) requires approximately 34.1 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 M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Cerebras-GPT 13B achieves approximately 50.6 tokens per second decode speed with a time-to-first-token of 3829ms using Q5_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Cerebras-GPT 13B for coding?

For coding workloads, Cerebras-GPT 13B on Mac Studio M2 Ultra 128GB receives a B grade with 50.6 tok/s and 111K context.

What context window can Cerebras-GPT 13B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Cerebras-GPT 13B can safely use up to 111K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Mac Studio M2 Ultra 128GB See all hardware for Cerebras-GPT 13B

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