MacBook Pro M4 Pro 64GB经济之选
64 GB Unified (+28)
B
Adds memory headroom for longer context windows and future model growth.20.2 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Can Baichuan 13B run on MacBook Pro M4 Max 36GB?
YES — With Offload
B66Good
Estimated from fit model
Baichuan 13B needs ~26.4 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q5_K_M quantization, expect ~27 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 26.4 GB, 27.1 tok/s, Runs with offload (needs ~0.2 GB host RAM)
26.4 GB required25.9 GB available
102% VRAM needed
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
27.1 tok/s
TTFT
7146 ms
Safe context
8K
Memory
26.4 GB / 25.9 GB
Memory breakdown
Weights9.4 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelsBaichuan 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: 27.1 tok/s decode · 7.1s TTFT (warm) · 68 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 | 8K |
| Coding | B | Runs with offload (needs ~0.2 GB host RAM) | 27.1 tok/s | 7146 ms | 8K |
| Agentic Coding | F | Too heavy | 16.6 tok/s | 16949 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0.2 GB host RAM) | 27.1 tok/s | 8445 ms | 8K |
| RAG | F | Too heavy | 16.6 tok/s | 21187 ms | 8K |
Quantization options
How Baichuan 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 Baichuan 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-13B-Chat" \
--hf-file "Baichuan-13B-Chat-Q5_K_M.gguf" \
-c 4096 -ngl 99升级选项
能流畅运行 Baichuan 13B 的硬件
MacBook Pro M4 Max 48GB最佳性价比
48 GB Unified (+12)546 GB/s (+136)
B
Adds memory headroom for longer context windows and future model growth.33 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
MacBook Pro M3 Max 48GBApple 升级
48 GB Unified (+12)
B
Adds memory headroom for longer context windows and future model growth.26.2 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 Max 36GB run Baichuan 13B?
Yes, MacBook Pro M4 Max 36GB can run Baichuan 13B with a B grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 27.1 tok/s.
How much VRAM does Baichuan 13B need?
Baichuan 13B (13B parameters) requires approximately 26.4 GB of memory with Q5_K_M quantization.
What is the best quantization for Baichuan 13B?
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
What speed will Baichuan 13B run at on MacBook Pro M4 Max 36GB?
On MacBook Pro M4 Max 36GB, Baichuan 13B achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7146ms using Q5_K_M quantization.
Can MacBook Pro M4 Max 36GB run Baichuan 13B for coding?
For coding workloads, Baichuan 13B on MacBook Pro M4 Max 36GB receives a B grade with 27.1 tok/s and 8K context.
What context window can Baichuan 13B use on MacBook Pro M4 Max 36GB?
On MacBook Pro M4 Max 36GB, Baichuan 13B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
What should I upgrade first if Baichuan 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 Baichuan 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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