Baichuan 13B needs ~25.1 GB but Mac mini M2 24GB only has 17.3 GB. Try a smaller quantization or lighter model.
Operating mode
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
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%
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Very compromised (needs ~0.8 GB host RAM) | 6.1 tok/s | 17408 ms | 6K |
| Coding | F | Too heavy | 4.3 tok/s | 44972 ms | 6K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 88345 ms | 6K |
| Reasoning | F | Too heavy | 4.3 tok/s | 53149 ms | 6K |
| RAG | F | Too heavy | 3.2 tok/s | 110431 ms | 6K |
How Baichuan 13B (13B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B64 |
Q3_K_S | 3 | 6.4 GB | Low | B65 |
NVFP4 | 4 | 7.3 GB | Medium | B66 |
Q4_K_M | 4 | 7.9 GB | Medium | B67 |
Q5_K_M | 5 | 9.4 GB | High | B67 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | B67 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
升级选项
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 60%.
~$799 MSRP
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
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 60%.
~$1,099 MSRP
RTX 5090 32GB最大飞跃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
No, Baichuan 13B requires more memory than Mac mini M2 24GB provides.
Baichuan 13B (13B parameters) requires approximately 25.1 GB of memory with Q5_K_M quantization.
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
On Mac mini M2 24GB, Baichuan 13B achieves approximately 4.3 tokens per second decode speed with a time-to-first-token of 44972ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on Mac mini M2 24GB receives a F grade with 4.3 tok/s and 6K context.
On Mac mini M2 24GB, Baichuan 13B can safely use up to 6K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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.
Not always. Mac mini M2 24GB 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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<iframe src="https://willitrunai.com/embed/baichuan-13b-on-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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