RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl1792 GB/s (+992)
B
Raises estimated decode speed by about 242%.164 tok/s Dekodierung
Raises estimated decode speed by about 242%.
ca. $9,999 MSRP
Can Cerebras-GPT 13B run on Mac Studio M1 Ultra 128GB?
YES — Runs Great
B64Good
Estimated from fit model
Cerebras-GPT 13B needs ~34.1 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q5_K_M quantization, expect ~48 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.
Select quantization to explore
Q5_K_M (High quality) — 34.1 GB, 47.9 tok/s, Runs well
34.1 GB required92.2 GB available
37% VRAM used
Fit status
Runs well
Decode
47.9 tok/s
TTFT
4038 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 M1 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: 47.9 tok/s decode · 4.0s TTFT (warm) · 120 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 | 47.9 tok/s | 2202 ms | 111K |
| Coding | B | Runs well | 47.9 tok/s | 4038 ms | 111K |
| Agentic Coding | B | Runs well | 47.9 tok/s | 5873 ms | 111K |
| Reasoning | B | Runs well | 47.9 tok/s | 4772 ms | 111K |
| RAG | B | Runs well | 47.9 tok/s | 7341 ms | 111K |
Quantization options
How Cerebras-GPT 13B (13B params) fits at each quantization level on Mac Studio M1 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 | 7.3 GB | Medium | B55 |
Q4_K_M | 4 | 7.9 GB | Medium | B56 |
Q5_K_M | 5 | 9.4 GB | High | B56 |
Q6_K | 6 | 10.7 GB | High | B56 |
Q8_0 | 8 | 13.9 GB | Very High | B56 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B58 |
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 99Upgrade-Optionen
Hardware, die Cerebras-GPT 13B gut ausführt
Frequently asked questions
Can Mac Studio M1 Ultra 128GB run Cerebras-GPT 13B?
Yes, Mac Studio M1 Ultra 128GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 47.9 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 M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, Cerebras-GPT 13B achieves approximately 47.9 tokens per second decode speed with a time-to-first-token of 4038ms using Q5_K_M quantization.
Can Mac Studio M1 Ultra 128GB run Cerebras-GPT 13B for coding?
For coding workloads, Cerebras-GPT 13B on Mac Studio M1 Ultra 128GB receives a B grade with 47.9 tok/s and 111K context.
What context window can Cerebras-GPT 13B use on Mac Studio M1 Ultra 128GB?
On Mac Studio M1 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.
Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for Cerebras-GPT 13B?
Not always. Mac Studio M1 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.
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