RX 7900 XT 20GBBudget pick
800 GB/s (+527)
C
Raises estimated decode speed by about 148%.87.4 tok/s decode
Raises estimated decode speed by about 148%.
~$899 MSRP
glm 4 9b chat 1m needs ~10.0 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~38 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
35.2 tok/s
TTFT
5496 ms
Safe context
126K
Memory
10.0 GB / 17.3 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 | 35.2 tok/s | 2998 ms | 126K |
| Coding | C | Runs well | 38.3 tok/s | 5056 ms | 126K |
| Agentic Coding | C | Runs well | 35.2 tok/s | 7994 ms | 126K |
| Reasoning | C | Runs well | 35.2 tok/s | 6495 ms | 126K |
| RAG | C | Runs well | 35.2 tok/s | 9992 ms | 126K |
How glm 4 9b chat 1m (9B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C47 |
Q3_K_S | 3 | 4.4 GB | Low | C48 |
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 148%.
~$899 MSRP
RTX A4500 20GBBest valueRaises estimated decode speed by about 158%.
~$2,000 MSRP
Yes, MacBook Pro M4 Pro 24GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 38.3 tok/s.
glm 4 9b chat 1m (9B parameters) requires approximately 10.0 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 M4 Pro 24GB, glm 4 9b chat 1m achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5056ms using Q4_K_M quantization.
For coding workloads, glm 4 9b chat 1m on MacBook Pro M4 Pro 24GB receives a C grade with 38.3 tok/s and 126K context.
On MacBook Pro M4 Pro 24GB, glm 4 9b chat 1m can safely use up to 126K 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-m4-pro-24gb" 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 |
| C48 |
Q4_K_M | 4 | 5.5 GB | Medium | C49 |
Q5_K_M | 5 | 6.5 GB | High | C49 |
Q6_K | 6 | 7.4 GB | High | C50 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C51 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Not always. MacBook Pro M4 Pro 24GB 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.