Can Baichuan 7B run on Mac mini M4 32GB?

YES — Runs Great

B68Good

Estimated — low-sample bucket· few comparable runs

Baichuan 7B needs ~16.4 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.4 GB, 18.6 tok/s, Runs well

16.4 GB required23.0 GB available

71% VRAM used

Fit status

Runs well

Decode

18.6 tok/s

TTFT

10400 ms

Safe context

Memory

16.4 GB / 23.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsBaichuan 7B 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: 18.6 tok/s decode · 10.4s TTFT (warm) · 47 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	18.6 tok/s	5673 ms	8K
Coding	B	Runs well	20.2 tok/s	9568 ms	8K
Agentic Coding	B	Runs with offload (needs ~0.2 GB host RAM)	16.9 tok/s	16640 ms	8K
Reasoning	B	Runs well	18.6 tok/s	12291 ms	8K
RAG	B	Runs with offload (needs ~0.2 GB host RAM)	16.9 tok/s	20801 ms

Quantization options

How Baichuan 7B (7B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B61
Q3_K_S	3	3.4 GB	Low	B61
NVFP4	4

Get started

Copy-paste commands to run Baichuan 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "baichuan-inc/Baichuan-7B" \
  --hf-file "Baichuan-7B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Baichuan 7B well

MacBook Pro M4 Max 36GBBudget pick

36 GB Unified (+4)410 GB/s (+290)

Raises estimated decode speed by about 254%.65.9 tok/s decode

Raises estimated decode speed by about 254%.

~$2,499 MSRP

MacBook Pro M4 Max 48GBBest value

48 GB Unified (+16)546 GB/s (+426)

Raises estimated decode speed by about 372%.87.8 tok/s decode

Raises estimated decode speed by about 372%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Frequently asked questions

Can Mac mini M4 32GB run Baichuan 7B?

Yes, Mac mini M4 32GB can run Baichuan 7B with a B grade (Runs well). Expected decode speed: 20.2 tok/s.

How much VRAM does Baichuan 7B need?

Baichuan 7B (7B parameters) requires approximately 16.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan 7B?

The recommended quantization for Baichuan 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan 7B run at on Mac mini M4 32GB?

On Mac mini M4 32GB, Baichuan 7B achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9568ms using Q4_K_M quantization.

Can Mac mini M4 32GB run Baichuan 7B for coding?

For coding workloads, Baichuan 7B on Mac mini M4 32GB receives a B grade with 20.2 tok/s and 8K context.

What context window can Baichuan 7B use on Mac mini M4 32GB?

On Mac mini M4 32GB, Baichuan 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 32GB as fast as VRAM for Baichuan 7B?

Not always. Mac mini 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.

See all results for Mac mini M4 32GB See all hardware for Baichuan 7B

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