MacBook Pro M4 Max 48GBコスパ重視
48 GB Unified (+12)546 GB/s (+136)
B
Adds memory headroom for longer context windows and future model growth.33 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Can Cerebras-GPT 13B run on MacBook Pro M4 Max 36GB?
YES — Tight Fit
B66Good
Estimated from fit model
Cerebras-GPT 13B needs ~24.2 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q5_K_M quantization, expect ~28 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
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) — 24.2 GB, 28.1 tok/s, Tight fit
24.2 GB required25.9 GB available
93% VRAM used
Fit status
Tight fit
Decode
28.1 tok/s
TTFT
6878 ms
Safe context
19K
Memory
24.2 GB / 25.9 GB
Memory breakdown
Weights9.4 GB
KV Cache9.8 GB
Runtime1.2 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelsCerebras-GPT 13B on MacBook Pro M4 Max 36GB
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: 28.1 tok/s decode · 6.9s TTFT (warm) · 70 tok/s prefill
What limits this setup
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.
Best improvement path
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 | Runs well | 28.1 tok/s | 3752 ms | 19K |
| Coding | B | Tight fit | 28.1 tok/s | 6878 ms | 19K |
| Agentic Coding | F | Too heavy | 19.2 tok/s | 14632 ms | 19K |
| Reasoning | B | Tight fit | 28.1 tok/s | 8128 ms | 19K |
| RAG | F | Too heavy | 19.2 tok/s | 18290 ms | 19K |
Quantization options
How Cerebras-GPT 13B (13B params) fits at each quantization level on MacBook Pro M4 Max 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 |
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 99アップグレードオプション
Cerebras-GPT 13Bを快適に動かすハードウェア
MacBook Pro M3 Max 48GBコスパ最良
48 GB Unified (+12)
B
Adds memory headroom for longer context windows and future model growth.26.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
MacBook Pro M3 Max 64GBAppleアップグレード
64 GB Unified (+28)
B
Adds memory headroom for longer context windows and future model growth.26.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 Max 36GB run Cerebras-GPT 13B?
Yes, MacBook Pro M4 Max 36GB can run Cerebras-GPT 13B with a B grade (Tight fit). Expected decode speed: 28.1 tok/s.
How much VRAM does Cerebras-GPT 13B need?
Cerebras-GPT 13B (13B parameters) requires approximately 24.2 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 Max 36GB?
On MacBook Pro M4 Max 36GB, Cerebras-GPT 13B achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6878ms using Q5_K_M quantization.
Can MacBook Pro M4 Max 36GB run Cerebras-GPT 13B for coding?
For coding workloads, Cerebras-GPT 13B on MacBook Pro M4 Max 36GB receives a B grade with 28.1 tok/s and 19K context.
What context window can Cerebras-GPT 13B use on MacBook Pro M4 Max 36GB?
On MacBook Pro M4 Max 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.
What should I upgrade first if Cerebras-GPT 13B feels slow on MacBook Pro M4 Max 36GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Is unified memory on MacBook Pro M4 Max 36GB as fast as VRAM for Cerebras-GPT 13B?
Not always. MacBook Pro M4 Max 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.
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<iframe src="https://willitrunai.com/embed/cerebras-gpt-13b-on-m4-max-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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