RX 7900 XTX 24GBBudget pick
960 GB/s (+560)
C
Raises estimated decode speed by about 214%.125.9 tok/s decode
Raises estimated decode speed by about 214%.
~$999 MSRP
glm 4 9b chat 1m needs ~10.9 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~40 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
40.1 tok/s
TTFT
4831 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 | 40.1 tok/s | 2635 ms | 200K |
| Coding | C | Runs well | 40.1 tok/s | 4831 ms | 200K |
| Agentic Coding | C | Runs well | 40.1 tok/s | 7027 ms | 200K |
| Reasoning | C | Runs well | 40.1 tok/s | 5710 ms | 200K |
| RAG | C | Runs well | 40.1 tok/s | 8784 ms | 200K |
How glm 4 9b chat 1m (9B params) fits at each quantization level on MacBook Pro M1 Max 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 214%.
~$999 MSRP
RTX 3090 24GBBest valueRaises estimated decode speed by about 150%.
~$1,499 MSRP
Yes, MacBook Pro M1 Max 32GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 40.1 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 M1 Max 32GB, glm 4 9b chat 1m achieves approximately 40.1 tokens per second decode speed with a time-to-first-token of 4831ms using Q4_K_M quantization.
For coding workloads, glm 4 9b chat 1m on MacBook Pro M1 Max 32GB receives a C grade with 40.1 tok/s and 200K context.
On MacBook Pro M1 Max 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-m1-max-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 M1 Max 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.