Can Baichuan 13B run on Mac mini M4 64GB?

YES — Runs Great

B64Good

Estimated — low-sample bucket· few comparable runs

Baichuan 13B needs ~29.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q5_K_M quantization, expect ~8 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

Q5_K_M (High quality) — 29.4 GB, 8.3 tok/s, Runs well

29.4 GB required46.1 GB available

64% VRAM used

Fit status

Runs well

Decode

8.3 tok/s

TTFT

23366 ms

Safe context

Memory

29.4 GB / 46.1 GB

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsBaichuan 13B on Mac mini M4 64GB
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: 8.3 tok/s decode · 23.4s TTFT (warm) · 21 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	8.3 tok/s	12745 ms	8K
Coding	B	Runs well	8.3 tok/s	23366 ms	8K
Agentic Coding	B	Tight fit	8.3 tok/s	33987 ms	8K
Reasoning	B	Runs well	8.3 tok/s	27614 ms	8K
RAG	B	Tight fit	8.3 tok/s	42484 ms	8K

Inference speed

Baichuan 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Baichuan 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q5_K_M	130.8	Fits
RTX 4090 24GB	24 GB	Q5_K_M	61.0	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	60.7	Fits
RX 7900 XTX 24GB	24 GB	Q5_K_M	52.4	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	50.6	Fits
RTX 3090 24GB	24 GB	Q5_K_M	48.7	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	47.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	33.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	33.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	26.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	24.0	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	20.6	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.2	Fits
RTX 4070 12GB	12 GB	Q5_K_M	7.2	Too big
RTX 3060 12GB	12 GB	Q5_K_M	4.5	Too big
RTX 4060 8GB	8 GB	Q5_K_M	3.2	Too big

Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Baichuan 13B (13B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B58
Q3_K_S	3	6.4 GB	Low	B58
NVFP4	4	7.3 GB	Medium	B58
Q4_K_M	4	7.9 GB	Medium	B59
Q5_K_M	5	9.4 GB	High	B59
Q6_K	6	10.7 GB	High	B59
Q8_0	8	13.9 GB	Very High	B60
F16Best for your GPU	16	26.7 GB	Maximum	B64

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

Opciones de mejora

Hardware que ejecuta bien Baichuan 13B

MacBook Pro M4 Max 96GBOpción económica

96 GB Unified (+32)546 GB/s (+426)

Sube la velocidad estimada de decodificación alrededor de un 298%.33 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 298%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBMejor relación calidad-precio

96 GB Unified (+32)819 GB/s (+699)

Sube la velocidad estimada de decodificación alrededor de un 631%.60.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 631%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Mac Studio M2 Ultra 128GBMejora Apple

128 GB Unified (+64)800 GB/s (+680)

Sube la velocidad estimada de decodificación alrededor de un 510%.50.6 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 510%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Baichuan 13B?

Yes, Mac mini M4 64GB can run Baichuan 13B with a B grade (Runs well). Expected decode speed: 8.3 tok/s.

How much VRAM does Baichuan 13B need?

Baichuan 13B (13B parameters) requires approximately 29.4 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 Mac mini M4 64GB?

On Mac mini M4 64GB, Baichuan 13B achieves approximately 8.3 tokens per second decode speed with a time-to-first-token of 23366ms using Q5_K_M quantization.

Can Mac mini M4 64GB run Baichuan 13B for coding?

For coding workloads, Baichuan 13B on Mac mini M4 64GB receives a B grade with 8.3 tok/s and 8K context.

What context window can Baichuan 13B use on Mac mini M4 64GB?

On Mac mini M4 64GB, 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.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Baichuan 13B?

Not always. Mac mini M4 64GB 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 64GB See all hardware for Baichuan 13B

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