Cerebras-GPT 13B needs ~24.2 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q5_K_M quantization, expect ~12 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
Tight fit
Decode
11.9 tok/s
TTFT
16224 ms
Safe context
19K
Memory
24.2 GB / 25.9 GB
This setup is broadly balanced for this model.
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.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 11.9 tok/s | 8850 ms | 19K |
| Coding | B | Tight fit | 11.9 tok/s | 16224 ms | 19K |
| Agentic Coding | F | Too heavy | 8.2 tok/s | 34515 ms | 19K |
| Reasoning | B | Tight fit | 11.9 tok/s | 19174 ms | 19K |
| RAG | F | Too heavy | 8.2 tok/s | 43144 ms | 19K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B61 |
Q3_K_S | 3 | 6.4 GB | Low | B62 |
NVFP4 | 4 | 7.3 GB | Medium | B62 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B63 |
Q6_K | 6 | 10.7 GB | High | B64 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
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 99Opciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 70%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,599 MSRP
Sube la velocidad estimada de decodificación alrededor de un 177%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Sube la velocidad estimada de decodificación alrededor de un 120%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run Cerebras-GPT 13B with a B grade (Tight fit). Expected decode speed: 11.9 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 24.2 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 MacBook Pro M3 Pro 36GB, Cerebras-GPT 13B achieves approximately 11.9 tokens per second decode speed with a time-to-first-token of 16224ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on MacBook Pro M3 Pro 36GB receives a B grade with 11.9 tok/s and 19K context.
On MacBook Pro M3 Pro 36GB, Cerebras-GPT 13B can safely use up to 19K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M3 Pro 36GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cerebras-gpt-13b-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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