Baichuan 13B needs ~32.8 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q5_K_M quantization, expect ~25 tok/s.
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
Fit status
Runs well
Decode
25.3 tok/s
TTFT
7658 ms
Safe context
8K
Memory
32.8 GB / 69.1 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 25.3 tok/s | 4177 ms | 8K |
| Coding | B | Runs well | 25.3 tok/s | 7658 ms | 8K |
| Agentic Coding | B | Runs well | 25.3 tok/s | 11138 ms | 8K |
| Reasoning | B | Runs well | 25.3 tok/s | 9050 ms | 8K |
| RAG | B | Runs well | 25.3 tok/s | 13923 ms | 8K |
How Baichuan 13B (13B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B56 |
Q3_K_S | 3 | 6.4 GB | Low | B56 |
NVFP4 | 4 |
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 99Upgrade options
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 89%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run Baichuan 13B with a B grade (Runs well). Expected decode speed: 25.3 tok/s.
Baichuan 13B (13B parameters) requires approximately 32.8 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 MacBook Pro M2 Max 96GB, Baichuan 13B achieves approximately 25.3 tokens per second decode speed with a time-to-first-token of 7658ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on MacBook Pro M2 Max 96GB receives a B grade with 25.3 tok/s and 8K context.
On MacBook Pro M2 Max 96GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/baichuan-13b-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
7.3 GB |
| Medium |
| B57 |
Q4_K_M | 4 | 7.9 GB | Medium | B57 |
Q5_K_M | 5 | 9.4 GB | High | B57 |
Q6_K | 6 | 10.7 GB | High | B57 |
Q8_0 | 8 | 13.9 GB | Very High | B58 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B60 |
Not always. MacBook Pro M2 Max 96GB 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.