MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 101%.51.3 tok/s decode
Raises estimated decode speed by about 101%.
~$2,499 MSRP
glm 4 9b chat 1m needs ~10.9 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~26 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
25.5 tok/s
TTFT
7592 ms
Safe context
200K
Memory
10.9 GB / 23.0 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 | C | Runs well | 25.5 tok/s | 4141 ms | 200K |
| Coding | C | Runs well | 25.5 tok/s | 7592 ms | 200K |
| Agentic Coding | C | Runs well | 25.5 tok/s | 11043 ms | 200K |
| Reasoning | C | Runs well | 25.5 tok/s | 8972 ms | 200K |
| RAG | C | Runs well | 25.5 tok/s | 13803 ms | 200K |
How glm 4 9b chat 1m (9B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C45 |
Q3_K_S | 3 | 4.4 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run glm 4 9b chat 1m on your machine.
Run
lms load hf-bartowski--glm-4-9b-chat-1m-gguf && lms server startUpgrade options
Raises estimated decode speed by about 101%.
~$2,499 MSRP
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 231%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M2 Pro 32GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 25.5 tok/s.
glm 4 9b chat 1m (9B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.
The recommended quantization for glm 4 9b chat 1m is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, glm 4 9b chat 1m achieves approximately 25.5 tokens per second decode speed with a time-to-first-token of 7592ms using Q4_K_M quantization.
For coding workloads, glm 4 9b chat 1m on MacBook Pro M2 Pro 32GB receives a C grade with 25.5 tok/s and 200K context.
On MacBook Pro M2 Pro 32GB, glm 4 9b chat 1m can safely use up to 200K tokens of context. The model's official context limit is —, 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/hf-bartowski--glm-4-9b-chat-1m-gguf-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| C46 |
Q4_K_M | 4 | 5.5 GB | Medium | C46 |
Q5_K_M | 5 | 6.5 GB | High | C47 |
Q6_K | 6 | 7.4 GB | High | C47 |
Q8_0 | 8 | 9.6 GB | Very High | C49 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C49 |
Not always. MacBook Pro M2 Pro 32GB 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.