Baichuan 13B needs ~27.7 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q5_K_M quantization, expect ~33 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
33.0 tok/s
TTFT
5869 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 | 33.0 tok/s | 3201 ms | 8K |
| Coding | B | Runs well | 33.0 tok/s | 5869 ms | 8K |
| Agentic Coding | B | Very compromised (needs ~1.2 GB host RAM) | 26.4 tok/s | 10658 ms | 8K |
| Reasoning | B | Runs well | 33.0 tok/s | 6936 ms | 8K |
| RAG | B | Very compromised (needs ~1.2 GB host RAM) | 26.4 tok/s | 13322 ms | 8K |
Inference speed
Estimated decode speed (tokens/sec) for Baichuan 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q5_K_M | 130.8 | Fits |
| RTX 4090 24GB | 24 GB | Q5_K_M | 61.0 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 52.4 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.6 | Fits |
| RTX 3090 24GB | 24 GB | Q5_K_M | 48.7 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 47.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 26.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 24.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 20.6 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.2 | Fits |
| RTX 4070 12GB | 12 GB | Q5_K_M | 7.2 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 4.5 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 3.2 | Too big |
Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Baichuan 13B (13B params) fits at each quantization level on MacBook Pro M4 Max 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 | 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 |
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 53%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
RTX PRO 5000 Blackwell 48GBBest valueRaises estimated decode speed by about 273%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Yes, MacBook Pro M4 Max 48GB can run Baichuan 13B with a B grade (Runs well). Expected decode speed: 33.0 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 Max 48GB, Baichuan 13B achieves approximately 33.0 tokens per second decode speed with a time-to-first-token of 5869ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on MacBook Pro M4 Max 48GB receives a B grade with 33.0 tok/s and 8K context.
On MacBook Pro M4 Max 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.
Not always. MacBook Pro M4 Max 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/baichuan-13b-on-m4-max-48gb" 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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