Mac mini M4 64GBBudget pick
64 GB Unified (+32)
B
Adds memory headroom for longer context windows and future model growth.8.3 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Cerebras-GPT 13B needs ~23.8 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q5_K_M quantization, expect ~9 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
7.8 tok/s
TTFT
24921 ms
Safe context
15K
Memory
23.8 GB / 23.0 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 | Tight fit | 8.3 tok/s | 12745 ms | 15K |
| Coding | B | Runs with offload | 8.8 tok/s | 21930 ms | 15K |
| Agentic Coding | F | Too heavy | 5.0 tok/s | 56183 ms | 15K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 7.8 tok/s | 29452 ms | 15K |
| RAG | F | Too heavy | 5.0 tok/s | 70228 ms | 15K |
How Cerebras-GPT 13B (13B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B62 |
Q3_K_S | 3 | 6.4 GB | Low | B63 |
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
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 159%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 549%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 32GB can run Cerebras-GPT 13B with a B grade (Runs with offload). Expected decode speed: 8.8 tok/s.
Cerebras-GPT 13B (13B parameters) requires approximately 23.8 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 mini M4 32GB, Cerebras-GPT 13B achieves approximately 8.8 tokens per second decode speed with a time-to-first-token of 21930ms using Q5_K_M quantization.
For coding workloads, Cerebras-GPT 13B on Mac mini M4 32GB receives a B grade with 8.8 tok/s and 15K context.
On Mac mini M4 32GB, Cerebras-GPT 13B can safely use up to 15K 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-mini-32gb" 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 |
| B63 |
Q4_K_M | 4 | 7.9 GB | Medium | B64 |
Q5_K_M | 5 | 9.4 GB | High | B65 |
Q6_K | 6 | 10.7 GB | High | B66 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
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. Mac mini M4 32GB 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.