NVIDIA GeForce RTX 5060 Laptop GPU for local LLMs
Written by Jakub Rusinowski · Last updated
With 8 GB of GDDR7 at 384 GB/s, the RTX 5060 Laptop GPU runs 53 catalogued models at Q4_K_M with 8K context. The largest that fits is Bielik PL 11B v3.0 Instruct (~7.4 GB), and the top pick is Qwen 3.5 9B at 29–60 tok/s.
8 GB of GDDR7 on a 128-bit bus, 384 GB/s. Power depends on the chassis and is not stored.
Models that run on the RTX 5060 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 | 6.2 GB | 29–60 tok/s | |
| GLM-4 9B GLM-4.7 / GLM-Z1 | 6.2 GB | 29–60 tok/s | |
| GLM-4.6V-Flash 9B GLM-4.6V | 6.2 GB | 29–60 tok/s | |
| EuroLLM 9B EuroLLM | 6.2 GB | 29–60 tok/s | |
| InternLM 3 8B Instruct InternLM 3 | 6.5 GB | 33–68 tok/s | |
| LFM2.5-8B-A1B LFM2.5 | 5.8 GB | 75–157 tok/s | |
| Qwen 3 8B Qwen 3 | 7 GB | 31–64 tok/s | |
| Aya Expanse 8B Aya Expanse | 6.7 GB | 32–66 tok/s | |
| Ministral 8B Ministral | 6.9 GB | 31–65 tok/s | |
| Gemma 4 E4B Gemma 4 | 5.6 GB | 32–66 tok/s | |
| Cogito v1 8B Cogito v1 | 6.7 GB | 32–66 tok/s | |
| Granite 4.1 8B IBM Granite 4.1 | 5.6 GB | 32–66 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 2026-10-07.
- Memory
- 8 GB GDDR7
- Memory bandwidth
- 384 GB/s
- Memory bus
- 128-bit
- Architecture
- Blackwell GB206
- Series
- RTX 50-series (Laptop)
- Release year
- 2025
- Compute backends
- CUDA
- Usable for models
- 8 GB
Similar GPUs
Frequently asked questions
Can the NVIDIA GeForce RTX 5060 Laptop GPU run local LLMs?
Yes. With 8 GB (8 GB usable by a model) it runs 53 of the catalogued models at Q4_K_M with 8K context; the largest is Bielik PL 11B v3.0 Instruct, needing about 7.4 GB.
How fast is the NVIDIA GeForce RTX 5060 Laptop GPU for AI inference?
It is estimated to run Llama 3.1 8B at 35–72 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, InternLM 3 8B Instruct.