MacBook Pro M4 Pro 64GBBudget pick
64 GB Unified (+28)
B
Adds memory headroom for longer context windows and future model growth.20.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Baichuan 13B needs ~26.4 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q5_K_M quantization, expect ~27 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
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
27.1 tok/s
TTFT
7146 ms
Safe context
8K
Memory
26.4 GB / 25.9 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 28.1 tok/s | 3752 ms | 8K |
| Coding | B | Runs with offload | 27.1 tok/s | 7146 ms | 8K |
| Agentic Coding | F | Too heavy | 16.6 tok/s | 16949 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0.2 GB host RAM) | 27.1 tok/s | 8445 ms | 8K |
| RAG | F | Too heavy | 16.6 tok/s | 21187 ms | 8K |
How Baichuan 13B (13B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B61 |
Q3_K_S | 3 | 6.4 GB | Low | B62 |
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
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 Max 36GB can run Baichuan 13B with a B grade (Runs with offload). Expected decode speed: 27.1 tok/s.
Baichuan 13B (13B parameters) requires approximately 26.4 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 M4 Max 36GB, Baichuan 13B achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7146ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on MacBook Pro M4 Max 36GB receives a B grade with 27.1 tok/s and 8K context.
On MacBook Pro M4 Max 36GB, 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-m4-max-36gb" 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 |
| B62 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B63 |
Q6_K | 6 | 10.7 GB | High | B64 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M4 Max 36GB 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.