NVIDIA GeForce RTX 4060 Laptop GPU for local LLMs
Written by Jakub Rusinowski · Last updated
With 8 GB of GDDR6 at 256 GB/s, the RTX 4060 Laptop GPU runs 61 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 26–50 tok/s.
8 GB GDDR6 on a 128-bit bus: 256 GB/s, against the desktop RTX 4060's 272 GB/s. Configurable from 35 W to 115 W, a range wide enough that the same model number performs very differently between thin and thick chassis.
Models that run on the RTX 4060 Laptop GPU
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 | 6.6 GB | 26–50 tok/s | |
| GLM-4 9B GLM-4.7 / GLM-Z1 | 6.2 GB | 23–44 tok/s | |
| GLM-4.6V-Flash 9B GLM-4.6V | 6.2 GB | 23–44 tok/s | |
| EuroLLM 9B EuroLLM | 6.2 GB | 23–44 tok/s | |
| MiMo-V2.6 Distill 9B MiMo-V2.6 | 6.5 GB | 26–50 tok/s | |
| Ternary Bonsai 2 9B Ternary Bonsai 2 | 6.5 GB | 26–50 tok/s | |
| InternLM 3 8B Instruct InternLM 3 | 6.5 GB | 26–50 tok/s | |
| LFM2.5-8B-A1B LFM2.5 | 5.8 GB | 62–119 tok/s | |
| Qwen 3 8B Qwen 3 | 7 GB | 24–47 tok/s | |
| Aya Expanse 8B Aya Expanse | 6.7 GB | 25–48 tok/s | |
| Ministral 8B Ministral | 6.9 GB | 25–47 tok/s | |
| Gemma 4 E4B Gemma 4 | 5.6 GB | 25–48 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 2026-09-19.
- Memory
- 8 GB GDDR6
- Memory bandwidth
- 256 GB/s
- Memory bus
- 128-bit
- Architecture
- Ada Lovelace AD107
- Series
- RTX 40-series (Laptop)
- Board power
- 115 W
- Laptop TGP range
- 35–115 W
- Release year
- 2023
- Compute backends
- CUDA
- Usable for models
- 8 GB
Similar GPUs
Frequently asked questions
Can the NVIDIA GeForce RTX 4060 Laptop GPU run local LLMs?
Yes. With 8 GB (8 GB usable by a model) it runs 61 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 Laptop GPU for AI inference?
It is estimated to run Llama 3.1 8B at 28–53 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.