glm 4 9b chat 1m needs ~8.2 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~19 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
18.8 tok/s
TTFT
10307 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 | 27.5 tok/s | 3838 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 18.8 tok/s | 10307 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 14.4 tok/s | 19529 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 18.8 tok/s | 12181 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 14.4 tok/s | 24411 ms | 12K |
How glm 4 9b chat 1m (9B params) fits at each quantization level on GTX 1070 Ti 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 | 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 |
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 startOpciones de mejora
RTX 3060 12GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 94%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
RTX 5060 Ti 16GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 169%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
RTX 4060 Ti 16GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 87%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$499 MSRP
Yes, GTX 1070 Ti 8GB can run glm 4 9b chat 1m with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 18.8 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 GTX 1070 Ti 8GB, glm 4 9b chat 1m achieves approximately 18.8 tokens per second decode speed with a time-to-first-token of 10307ms using Q4_K_M quantization.
For coding workloads, glm 4 9b chat 1m on GTX 1070 Ti 8GB receives a C grade with 18.8 tok/s and 12K context.
On GTX 1070 Ti 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-gtx-1070-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: