RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick1792 GB/s (+992)
B
Raises estimated decode speed by about 224%.164 tok/s decode
Raises estimated decode speed by about 224%.
~$9,999 MSRP
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.
Operating mode
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
Fit status
Runs well
Decode
50.6 tok/s
TTFT
3829 ms
Safe context
111K
Memory
34.1 GB / 92.2 GB
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.
| 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 |
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 |
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 options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 224%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 189%.
~$9,999 MSRP
Yes, Mac Studio M2 Ultra 128GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 50.6 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 34.1 GB of memory with Q5_K_M quantization.
The recommended quantization for Cerebras-GPT 13B is Q5_K_M, which balances quality and memory efficiency.
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.
For coding workloads, Cerebras-GPT 13B on Mac Studio M2 Ultra 128GB receives a B grade with 50.6 tok/s and 111K context.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cerebras-gpt-13b-on-m2-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
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.