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
Can Cerebras-GPT 13B run on Mac mini M4 32GB?
YES — With Offload
B62Good
○Estimated from fit model
Cerebras-GPT 13B needs ~23.8 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q5_K_M quantization, expect ~8 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
Share:
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) — 23.8 GB, 7.8 tok/s, Runs with offload (needs ~0.3 GB host RAM)
23.8 GB required23.0 GB available
103% VRAM needed
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
Memory breakdown
Weights9.4 GB
KV Cache9.8 GB
Runtime1.2 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsCerebras-GPT 13B on Mac mini M4 32GB
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: 7.8 tok/s decode · 24.9s TTFT (warm) · 19 tok/s prefill
What limits this setup
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Best improvement path
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 8.3 tok/s | 12745 ms | 15K |
| Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 7.8 tok/s | 24921 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 |
Quantization options
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 | 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 |
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 options
Hardware that runs Cerebras-GPT 13B well
MacBook Pro M4 Pro 64GBBest value
64 GB Unified (+32)273 GB/s (+153)
B
Raises estimated decode speed by about 159%.20.2 tok/s decode
Raises estimated decode speed by about 159%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Mac Studio M2 Ultra 64GBApple upgrade
64 GB Unified (+32)800 GB/s (+680)
B
Raises estimated decode speed by about 549%.50.6 tok/s decode
Raises estimated decode speed by about 549%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP