Cerebras-GPT 13B needs ~30.7 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q5_K_M quantization, expect ~33 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
33.0 tok/s
TTFT
5869 ms
Safe context
79K
Memory
30.7 GB / 69.1 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 | 33.0 tok/s | 3201 ms | 79K |
| Coding | B | Runs well | 33.0 tok/s | 5869 ms | 79K |
| Agentic Coding | B | Runs well | 33.0 tok/s | 8537 ms | 79K |
| Reasoning | B | Runs well | 33.0 tok/s | 6936 ms | 79K |
| RAG | B | Runs well | 33.0 tok/s | 10671 ms | 79K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on MacBook Pro M4 Max 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 |
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 397%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 343%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
Yes, MacBook Pro M4 Max 96GB can run Cerebras-GPT 13B with a B grade (Runs well). Expected decode speed: 33.0 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 30.7 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 M4 Max 96GB, Cerebras-GPT 13B achieves approximately 33.0 tokens per second decode speed with a time-to-first-token of 5869ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on MacBook Pro M4 Max 96GB receives a B grade with 33.0 tok/s and 79K context.
On MacBook Pro M4 Max 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/cerebras-gpt-13b-on-m4-max-96gb" 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 |
| 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 |
Not always. MacBook Pro M4 Max 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.