RX 7600 XT 16GBBudget pick
16 GB VRAM (+8)288 GB/s (+32)
C
Raises estimated decode speed by about 122%.30.4 tok/s decode
Raises estimated decode speed by about 122%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
glm 4 9b chat 1m needs ~8.2 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~14 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
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
13.7 tok/s
TTFT
14143 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
This setup is broadly balanced for this model.
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 20.1 tok/s | 5267 ms | 12K |
| Coding | C | Runs with offload | 13.7 tok/s | 14143 ms | 12K |
| Agentic Coding | D | Very compromised | 10.5 tok/s | 26796 ms | 12K |
| Reasoning | C | Runs with offload | 13.7 tok/s | 16714 ms | 12K |
| RAG | D | Very compromised | 10.5 tok/s | 33494 ms | 12K |
How glm 4 9b chat 1m (9B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C54 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4Best for your GPU |
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 122%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Raises estimated decode speed by about 245%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Yes, RX 590 8GB can run glm 4 9b chat 1m with a C grade (Runs with offload). Expected decode speed: 13.7 tok/s.
glm 4 9b chat 1m (9B parameters) requires approximately 8.2 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 RX 590 8GB, glm 4 9b chat 1m achieves approximately 13.7 tokens per second decode speed with a time-to-first-token of 14143ms using Q4_K_M quantization.
For coding workloads, glm 4 9b chat 1m on RX 590 8GB receives a C grade with 13.7 tok/s and 12K context.
On RX 590 8GB, glm 4 9b chat 1m can safely use up to 12K 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-rx-590-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
5.0 GB |
| Medium |
| C53 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.