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URL: https://willitrunai.com/can-run/baichuan-13b-on-m2-24gb

⇱ Baichuan 13B on Mac mini M2 24GB? No — Alternatives

Can Baichuan 13B run on Mac mini M2 24GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Baichuan 13B needs ~25.1 GB but Mac mini M2 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity
Share:

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) — 25.1 GB, exceeds 17.3 GB available
25.1 GB required17.3 GB available
145% VRAM needed

7.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.3 tok/s

TTFT

44972 ms

Safe context

6K

Memory

25.1 GB / 17.3 GB

Offload

30%

Memory breakdown

Weights9.4 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsBaichuan 13B on Mac mini M2 24GB
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: 4.3 tok/s decode · 45.0s TTFT (warm) · 11 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 25.1 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCVery compromised (needs ~0.8 GB host RAM)6.1 tok/s17408 ms6K
CodingFToo heavy4.3 tok/s44972 ms6K
Agentic CodingFToo heavy3.2 tok/s88345 ms6K
ReasoningFToo heavy4.3 tok/s53149 ms6K
RAGFToo heavy3.2 tok/s110431 ms6K

Quantization options

How Baichuan 13B (13B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowB64
Q3_K_S
3
6.4 GB
LowB65
NVFP4
4
7.3 GB
MediumB66
Q4_K_M
4
7.9 GB
MediumB67
Q5_K_M
5
9.4 GB
HighB67
Q6_KBest for your GPU
6
10.7 GB
HighB67
Q8_0
8
13.9 GB
Very HighF0
F16
16
26.7 GB
MaximumF0

Upgrade options

Hardware that runs Baichuan 13B well

MacBook Pro M4 32GBBest value
32 GB Unified (+8)120 GB/s (+20)
C
Makes the model fit on the accelerator instead of staying completely out of reach.6.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 60%.

~$799 MSRP

Mac mini M4 64GBBudget pick
64 GB Unified (+40)120 GB/s (+20)
B
Makes the model fit on the accelerator instead of staying completely out of reach.8.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

Mac mini M4 32GBApple upgrade
32 GB Unified (+8)120 GB/s (+20)
C
Makes the model fit on the accelerator instead of staying completely out of reach.6.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 60%.

~$1,099 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap
32 GB VRAM (+8)1792 GB/s (+1692)
A
Makes the model fit on the accelerator instead of staying completely out of reach.126.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

See all results for Mac mini M2 24GBSee all hardware for Baichuan 13B