Baichuan M2 32B Q4 K M needs ~28.1 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~12 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
2.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.5 GB host RAM)
Decode
21.4 tok/s
TTFT
9045 ms
Safe context
7K
Memory
28.1 GB / 25.9 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 12.9 tok/s | 8187 ms | 7K |
| Coding | D | Very compromised | 11.5 tok/s | 16769 ms | 7K |
| Agentic Coding | F | Too heavy | 9.8 tok/s | 28734 ms | 7K |
| Reasoning | D | Very compromised | 11.5 tok/s | 19818 ms | 7K |
| RAG | F | Too heavy | 9.8 tok/s | 35917 ms | 7K |
How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C50 |
Q3_K_S | 3 | 15.7 GB | Low | C49 |
NVFP4 | 4 | 17.9 GB | Medium | C49 |
Q4_K_MBest for your GPU | 4 | 19.5 GB | Medium | C49 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Copy-paste commands to run Baichuan M2 32B Q4 K M on your machine.
Run
lms load hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf && lms server startアップグレードオプション
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
〜$1,099 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 44%.
〜$2,499 MSRP
Yes, MacBook Pro M4 Max 36GB can run Baichuan M2 32B Q4 K M with a D grade (Very compromised). Expected decode speed: 11.5 tok/s.
Baichuan M2 32B Q4 K M (32B parameters) requires approximately 28.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan M2 32B Q4 K M is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 36GB, Baichuan M2 32B Q4 K M achieves approximately 11.5 tokens per second decode speed with a time-to-first-token of 16769ms using Q4_K_M quantization.
For coding workloads, Baichuan M2 32B Q4 K M on MacBook Pro M4 Max 36GB receives a D grade with 11.5 tok/s and 7K context.
On MacBook Pro M4 Max 36GB, Baichuan M2 32B Q4 K M can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
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