RX 7900 XT 20GBBest value
20 GB VRAM (+4)800 GB/s (+512)
C
Raises estimated decode speed by about 119%.87.4 tok/s decode
Raises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
glm 4 9b chat 1m needs ~9.3 GB VRAM. RTX 2000 Ada 16GB has 16.0 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
39.9 tok/s
TTFT
4856 ms
Safe context
117K
Memory
9.3 GB / 16.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 39.9 tok/s | 2649 ms | 117K |
| Coding | C | Runs well | 39.9 tok/s | 4856 ms | 117K |
| Agentic Coding | C | Runs well | 39.9 tok/s | 7063 ms | 117K |
| Reasoning | C | Runs well | 39.9 tok/s | 5739 ms | 117K |
| RAG | C | Runs well | 39.9 tok/s | 8829 ms | 117K |
How glm 4 9b chat 1m (9B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 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 | 5.0 GB | Medium | C49 |
Q4_K_M | 4 | 5.5 GB | Medium | C49 |
Q5_K_M | 5 | 6.5 GB | High | C50 |
Q6_K | 6 | 7.4 GB | High | C51 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C51 |
F16 | 16 | 18.5 GB | Maximum | F0 |
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 119%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
RTX A4500 20GBBudget pickRaises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
~$2,000 MSRP
Yes, RTX 2000 Ada 16GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 39.9 tok/s.
glm 4 9b chat 1m (9B parameters) requires approximately 9.3 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 RTX 2000 Ada 16GB, glm 4 9b chat 1m achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4856ms using Q4_K_M quantization.
For coding workloads, glm 4 9b chat 1m on RTX 2000 Ada 16GB receives a C grade with 39.9 tok/s and 117K context.
On RTX 2000 Ada 16GB, glm 4 9b chat 1m can safely use up to 117K 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-rtx-2000-ada-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: