GPUs · NVIDIA · Laptop
Can the RTX 3060 Laptop run local LLMs?
With 6 GB of VRAM (about 5.4 GB usable) and 336 GB/s of memory bandwidth, the RTX 3060 Laptop runs 8 of 39 popular open models fully on the GPU at 8K context. The largest is Qwen3.5 4B at Q6_K, at roughly 57 tokens per second.
Best picks for the RTX 3060 Laptop
Every model on the RTX 3060 Laptop
Results at 8K context, picking the highest quality quantization that fits. "Offload" means part of the model runs from system RAM (we assume 32 GB), which works but is much slower.
| Model | Status | Quant | Memory | Speed |
|---|---|---|---|---|
| Qwen3.5 4B | Runs well | Q6_K | 5.1 GB | ~57 tok/s |
| Gemma 3 4B | Runs well | Q5_K_M | 5.3 GB | ~72 tok/s |
| Qwen3 4B | Runs well | Q6_K | 4.7 GB | ~67 tok/s |
| Phi-4 Mini 3.8B | Runs well | Q8_0 | 5.2 GB | ~54 tok/s |
| Llama 3.2 3B | Runs well | Q8_0 | 4.6 GB | ~64 tok/s |
| Llama 3.2 1B | Runs well | Q8_0 | 1.6 GB | ~170 tok/s |
| Gemma 3 1B | Runs well | Q8_0 | 1.4 GB | ~210 tok/s |
| Qwen3 0.6B | Runs well | Q8_0 | 0.9 GB | ~340 tok/s |
| Mistral 7B v0.3 | Offload | Q4_K_M | 5.7 GB | ~7.7 tok/s |
| Qwen2.5-Coder 7B | Offload | Q4_K_M | 5.8 GB | ~7.3 tok/s |
| Llama 3.1 8B | Offload | Q4_K_M | 6.2 GB | ~7.0 tok/s |
| DeepSeek-R1 Distill 8B | Offload | Q4_K_M | 6.2 GB | ~7.0 tok/s |
| Qwen3 8B | Offload | Q4_K_M | 6.4 GB | ~6.8 tok/s |
| Qwen3.5 9B | Offload | Q4_K_M | 7.0 GB | ~6.0 tok/s |
| Mistral Nemo 12B | Offload | Q4_K_M | 9.2 GB | ~4.7 tok/s |
| Gemma 4 12B | Offload | Q4_K_M | 11 GB | ~4.8 tok/s |
| Phi-4 14B | Offload | Q4_K_M | 11 GB | ~3.9 tok/s |
| Gemma 3 12B | Offload | Q4_K_M | 11 GB | ~4.7 tok/s |
| Qwen3 14B | Offload | Q4_K_M | 11 GB | ~3.9 tok/s |
| DeepSeek-R1 Distill 14B | Offload | Q4_K_M | 11 GB | ~3.9 tok/s |
| gpt-oss 20B | Offload | Q4_K_M | 14 GB | ~11 tok/s |
| Codestral 22B | Offload | Q4_K_M | 16 GB | ~2.6 tok/s |
| Devstral Small 24B | Offload | Q4_K_M | 17 GB | ~2.4 tok/s |
| Mistral Small 3 24B | Offload | Q4_K_M | 18 GB | ~2.4 tok/s |
| Gemma 4 26B-A4B (MoE) | Offload | Q4_K_M | 19 GB | ~10 tok/s |
| Qwen3.6 27B | Offload | Q4_K_M | 19 GB | ~2.1 tok/s |
| Qwen3 30B-A3B (MoE) | Offload | Q4_K_M | 21 GB | ~11 tok/s |
| Qwen3-Coder 30B-A3B | Offload | Q4_K_M | 21 GB | ~11 tok/s |
| Gemma 4 31B | Offload | Q3_K_M | 21 GB | ~2.3 tok/s |
| Gemma 3 27B | Offload | Q4_K_M | 22 GB | ~2.1 tok/s |
| Qwen3 32B | Offload | Q4_K_M | 23 GB | ~1.8 tok/s |
| DeepSeek-R1 Distill 32B | Offload | Q4_K_M | 23 GB | ~1.8 tok/s |
| Qwen2.5-Coder 32B | Offload | Q4_K_M | 23 GB | ~1.8 tok/s |
| Qwen3.6 35B-A3B (MoE) | Offload | Q4_K_M | 23 GB | ~12 tok/s |
| Llama 3.3 70B | Won’t fit | — | 48 GB | — |
| DeepSeek-R1 Distill 70B | Won’t fit | — | 48 GB | — |
| Llama 4 Scout 109B-A17B | Won’t fit | — | 73 GB | — |
| gpt-oss 120B | Won’t fit | — | 77 GB | — |
| Qwen3 235B-A22B (MoE) | Won’t fit | — | 155 GB | — |
What this means in practice
The RTX 3060 Laptop is comfortable with models up to about 4B parameters. gpt-oss 20B is the most useful step up beyond that: it needs about 14 GB, so part of it runs from system RAM, but as a mixture-of-experts model it stays reasonably quick at around 11 tokens per second. Models from Llama 3.3 70B upward need 48 GB or more and are out of reach. Laptop versions run at lower power than desktop cards, so expect speeds at the lower end and less headroom if the laptop is also driving a display. NVIDIA cards have the broadest support: Ollama, LM Studio and llama.cpp all use CUDA out of the box.
The next memory tier up is 8 GB: Arc A750, RTX 5060 Ti, RTX 5060. Other 6 GB cards: GTX 1660 / 1660 Super, RTX 4050 Laptop.
Compare the RTX 3060 Laptop
Questions
What is the biggest model the RTX 3060 Laptop can run?
At 8K context the largest model that fits entirely in its 6 GB of VRAM is Qwen3.5 4B at Q6_K, at roughly 57 tokens per second.
Can the RTX 3060 Laptop run 70B models like Llama 3.3 70B?
No. It needs about 48 GB at Q4, which is far beyond 6 GB even with system RAM offload.
What is the best model for coding on the RTX 3060 Laptop?
Qwen3 4B at Q6_K fits fully and runs at about 67 tokens per second. Pair it with an editor extension like Continue pointed at Ollama.
Estimates based on memory size and bandwidth; real speed varies with drivers, software and context length. See how we calculate VRAM.