Mac mini M4 64GBOpção econômica
64 GB Unified (+32)
B
Adds memory headroom for longer context windows and future model growth.8.3 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Can Cerebras-GPT 13B run on MacBook Pro M4 32GB?
YES — With Offload
B62Good
Estimated from fit model
Cerebras-GPT 13B needs ~23.8 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q5_K_M quantization, expect ~8 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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 MacBook Pro 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 MacBook Pro 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 99Opções de upgrade
Hardware que roda bem Cerebras-GPT 13B
MacBook Pro M4 Pro 64GBMelhor custo-benefício
64 GB Unified (+32)273 GB/s (+153)
B
Raises estimated decode speed by about 159%.20.2 tok/s decodificação
Raises estimated decode speed by about 159%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
MacBook Pro M3 Pro 36GBUpgrade Apple
36 GB Unified (+4)150 GB/s (+30)
B
Raises estimated decode speed by about 53%.11.9 tok/s decodificação
Raises estimated decode speed by about 53%.
~$1,999 MSRP
RTX 5090 32GBMaior salto1792 GB/s (+1672)
A
Raises estimated decode speed by about 1577%.130.8 tok/s decodificação
Raises estimated decode speed by about 1577%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Frequently asked questions
Can MacBook Pro M4 32GB run Cerebras-GPT 13B?
Yes, MacBook Pro M4 32GB can run Cerebras-GPT 13B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 7.8 tok/s.
How much VRAM does Cerebras-GPT 13B need?
Cerebras-GPT 13B (13B parameters) requires approximately 23.8 GB of memory with Q5_K_M quantization.
What is the best quantization for Cerebras-GPT 13B?
The recommended quantization for Cerebras-GPT 13B is Q5_K_M, which balances quality and memory efficiency.
What speed will Cerebras-GPT 13B run at on MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, Cerebras-GPT 13B achieves approximately 7.8 tokens per second decode speed with a time-to-first-token of 24921ms using Q5_K_M quantization.
Can MacBook Pro M4 32GB run Cerebras-GPT 13B for coding?
For coding workloads, Cerebras-GPT 13B on MacBook Pro M4 32GB receives a B grade with 7.8 tok/s and 15K context.
What context window can Cerebras-GPT 13B use on MacBook Pro M4 32GB?
On MacBook Pro 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.
What should I upgrade first if Cerebras-GPT 13B feels slow on MacBook Pro M4 32GB?
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.
Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Cerebras-GPT 13B?
Not always. MacBook Pro 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.
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