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: Host offload

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.

Capabilities:

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

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	27.1 tok/s	7146 ms	8K
Agentic Coding	F	Too heavy	16.6 tok/s	16949 ms	8K
Reasoning	B	Runs with offload	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

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

Upgrade options