RTX 3060 12GBOpción económica12 GB VRAM (+2)
C
Este setup está bastante equilibrado para este modelo.43.3 tok/s decodificación
~$329 MSRP
Can glm 4 9b chat 1m run on RTX 3080 10GB?
YES — Tight Fit
C54Usable
Estimated from fit model
glm 4 9b chat 1m needs ~8.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~105 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 8.7 GB, 105.2 tok/s, Tight fit
8.7 GB required10.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
105.2 tok/s
TTFT
1840 ms
Safe context
35K
Memory
8.7 GB / 10.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsglm 4 9b chat 1m on RTX 3080 10GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 105.2 tok/s decode · 1.8s TTFT (warm) · 263 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 105.2 tok/s | 1004 ms | 35K |
| Coding | C | Tight fit | 105.2 tok/s | 1840 ms | 35K |
| Agentic Coding | C | Runs with offload | 105.2 tok/s | 2677 ms | 35K |
| Reasoning | C | Tight fit | 105.2 tok/s | 2175 ms | 35K |
| RAG | C | Runs with offload | 105.2 tok/s | 3346 ms | 35K |
Quantization options
How glm 4 9b chat 1m (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C52 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4 | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 6.5 GB | High | C52 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
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
Hardware que ejecuta bien glm 4 9b chat 1m
RTX 3060 12GBOpción económicaRTX 5070 12GBMejor relación calidad-precio12 GB VRAM (+2)
B
Este setup está bastante equilibrado para este modelo.77.1 tok/s decodificación
~$549 MSRP
RTX 4070 Super 12GBMejora NVIDIA12 GB VRAM (+2)
B
Este setup está bastante equilibrado para este modelo.70.7 tok/s decodificación
~$599 MSRP
Frequently asked questions
Can RTX 3080 10GB run glm 4 9b chat 1m?
Yes, RTX 3080 10GB can run glm 4 9b chat 1m with a C grade (Tight fit). Expected decode speed: 105.2 tok/s.
How much VRAM does glm 4 9b chat 1m need?
glm 4 9b chat 1m (9B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
What is the best quantization for glm 4 9b chat 1m?
The recommended quantization for glm 4 9b chat 1m is Q4_K_M, which balances quality and memory efficiency.
What speed will glm 4 9b chat 1m run at on RTX 3080 10GB?
On RTX 3080 10GB, glm 4 9b chat 1m achieves approximately 105.2 tokens per second decode speed with a time-to-first-token of 1840ms using Q4_K_M quantization.
Can RTX 3080 10GB run glm 4 9b chat 1m for coding?
For coding workloads, glm 4 9b chat 1m on RTX 3080 10GB receives a C grade with 105.2 tok/s and 35K context.
What context window can glm 4 9b chat 1m use on RTX 3080 10GB?
On RTX 3080 10GB, glm 4 9b chat 1m can safely use up to 35K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--glm-4-9b-chat-1m-gguf-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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