Baichuan 13B needs ~27.7 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q5_K_M quantization, expect ~20 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
20.2 tok/s
TTFT
9604 ms
Safe context
8K
Memory
27.7 GB / 34.6 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 | 20.2 tok/s | 5238 ms | 8K |
| Coding | B | Runs well | 20.2 tok/s | 9604 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~1.2 GB host RAM) | 16.1 tok/s | 17440 ms | 8K |
| Reasoning | B | Runs well | 20.2 tok/s | 11350 ms | 8K |
| RAG | C | Very compromised (needs ~1.2 GB host RAM) | 16.1 tok/s | 21800 ms |
How Baichuan 13B (13B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B59 |
Q3_K_S | 3 | 6.4 GB | Low | B60 |
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 150%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 137%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 63%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Pro 48GB can run Baichuan 13B with a B grade (Runs well). Expected decode speed: 20.2 tok/s.
Baichuan 13B (13B parameters) requires approximately 27.7 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 Pro 48GB, Baichuan 13B achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9604ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on MacBook Pro M4 Pro 48GB receives a B grade with 20.2 tok/s and 8K context.
On MacBook Pro M4 Pro 48GB, 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-pro-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
7.3 GB |
| Medium |
| B60 |
Q4_K_M | 4 | 7.9 GB | Medium | B60 |
Q5_K_M | 5 | 9.4 GB | High | B61 |
Q6_K | 6 | 10.7 GB | High | B62 |
Q8_0 | 8 | 13.9 GB | Very High | B63 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B65 |
Not always. MacBook Pro M4 Pro 48GB 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.