NVIDIA GeForce RTX 4060 for local LLMs
Written by Jakub Rusinowski · Last updated
With 8 GB at 272 GB/s, the RTX 4060 runs 59 catalogued models at Q4_K_M with 8K context. The largest that fits is K2 Horizon 7B (~7.4 GB), and the top pick is Qwen 3.5 9B at 28–53 tok/s.
Great entry-level AI GPU. 8 GB VRAM is enough for any 7–8B model in Q4 quantization. Only 115W TDP makes it ideal for always-on AI servers.
Models that run on the RTX 4060
Q4_K_M, 8K context, 8 GB usable. Ranked by quality and speed.
| Model | Memory · marker = 8 GB | VRAM | Speed |
|---|---|---|---|
| Qwen 3.5 9B Qwen 3.5 | 6.5 GB | 28–53 tok/s | |
| GLM-4 9B GLM-4.7 / GLM-Z1 | 6.2 GB | 24–46 tok/s | |
| GLM-4.6V-Flash 9B GLM-4.6V | 6.2 GB | 24–46 tok/s | |
| EuroLLM 9B EuroLLM | 6.2 GB | 24–46 tok/s | |
| MiMo-V2.6 Distill 9B MiMo-V2.6 | 6.5 GB | 28–53 tok/s | |
| Ternary Bonsai 2 9B Ternary Bonsai 2 | 6.5 GB | 28–53 tok/s | |
| InternLM 3 8B Instruct InternLM 3 | 6.5 GB | 27–53 tok/s | |
| LFM2.5-8B-A1B LFM2.5 | 5.8 GB | 65–125 tok/s | |
| Qwen 3 8B Qwen 3 | 7 GB | 26–49 tok/s | |
| Aya Expanse 8B Aya Expanse | 6.7 GB | 27–51 tok/s | |
| Ministral 8B Ministral | 6.9 GB | 26–50 tok/s | |
| Gemma 4 E4B Gemma 4 | 5.6 GB | 26–51 tok/s |
Buy it or rent the same memory
Buy the card, or rent a GPU with the same memory by the hour to try models first.
or compare on Vast.ai from $0.35/hr (typical low · varies)
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Speed vs other GPUs
Llama 3.1 8B, Q4_K_M. Estimated ranges. How this is calculated
Specifications
Specs last updated 2026-07-12.
- Memory
- 8 GB
- Memory bandwidth
- 272 GB/s
- Architecture
- Ada Lovelace AD107
- Series
- RTX 40-series
- Board power
- 115 W
- Release year
- 2023
- Launch price
- $299
- Usable for models
- 8 GB
Similar GPUs
Frequently asked questions
Can the NVIDIA GeForce RTX 4060 run local LLMs?
Yes. With 8 GB (8 GB usable by a model) it runs 59 of the catalogued models at Q4_K_M with 8K context; the largest is K2 Horizon 7B, needing about 7.4 GB.
How fast is the NVIDIA GeForce RTX 4060 for AI inference?
It is estimated to run Llama 3.1 8B at 29–56 tok/s at Q4_K_M. Llama 3.3 70B does not fit: it needs about 44 GB against 8 GB usable. These are modelled estimates from memory bandwidth, not measurements; the methodology page shows the formula.
What LLMs can I run on 8 GB?
Among the best that fit: Qwen 3.5 9B, GLM-4 9B, GLM-4.6V-Flash 9B, EuroLLM 9B, MiMo-V2.6 Distill 9B. The quickest start is Ollama: ollama run llama3.1:8b.
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Can I run it on the RTX 4060?
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