Laptop GPU · NVIDIA
What AI models can a GeForce RTX 4060 Laptop run?
8 GB is the awkward size. It runs the 7 to 9 billion parameter class comfortably and hits a wall immediately above it, so most of the decisions here are about context length rather than model choice. Bandwidth is the weak point at 256 GB/s. Models fit, then generate slowly, because every token means reading the whole active model out of memory.
The short answer
Assuming an 8k context window and default settings, these are the models worth downloading first.
See how fast it feels
GeForce RTX 4060 Laptop running Gemma 4 12B Instruct at IQ4_XS
YouWhy does my model use more memory when the conversation gets longer?
Because of the KV cache. Every token you send leaves behind a key and a value vector in each layer of the model, and those stay in memory for as long as the conversation lasts.
The weights are a fixed cost: load a 4-bit 8B model and that is about 4.8 GB, whether you write one word or ten thousand. The cache is the part that grows, and it grows in a straight line with the number of tokens in the window.
How steeply depends on the model's attention design. With grouped-query attention, several query heads share one key-value pair, which cuts the cache by that ratio. Without it, every head keeps its own, and a long context can cost more memory than the weights themselves.
If you are short on memory, the first thing to try is lowering the context window in your runtime, not the quantisation.
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.
Every model, scored on this device
Each row uses the highest-quality quantisation that both fits and stays conversational. Speed is a single-stream estimate at 8k context.
| Model | Params | Verdict | Download | Memory used | Speed | Max context |
|---|---|---|---|---|---|---|
| Gemma 4 12B Instruct Gemma | 11.96B | Just fits | IQ4_XS | 7.13 GB | 33.4 t/s | 8k |
| Gemma 4 E4B Instruct Gemma | 8B | Just fits | Q6_K | 6.91 GB | 34.1 t/s | 16k |
| Internlm3 8B Instruct InternLM | 8.8B | Just fits | Q5_K_M | 7.06 GB | 33.8 t/s | 8k |
| LFM2.5 8B A1B Liquid | 8.47B (1.57B active) | Just fits | Q5_K_M | 6.71 GB | 148.4 t/s | 8k |
| Qwen2.5 7B Instruct Qwen | 7.62B | Just fits | Q6_K | 7.08 GB | 33.6 t/s | 8k |
| Qwen2.5 VL 7B Instruct Qwen | 8.29B | Just fits | Q5_K_M | 6.75 GB | 35.4 t/s | 8k |
| Llama 3.1 8B Instruct Llama | 8.03B | Just fits | Q5_K_M | 7.17 GB | 33.2 t/s | 8k |
| Apertus 8B Instruct 2509 Apertus | 8.05B | Just fits | Q5_K_M | 7.18 GB | 33.2 t/s | 8k |
| Llama 3 Taiwan 8B Instruct Llama | 8.03B | Just fits | Q5_K_M | 7.17 GB | 33.2 t/s | 8k |
| OLMoE 1B 7B 0125 Instruct OLMo | 6.92B (1.28B active) | Just fits | Q6_K | 7.01 GB | 106.2 t/s | 4k |
| Granite 4.1 8B Granite | 8.79B | Just fits | Q4_K_M | 7.04 GB | 33.9 t/s | 8k |
| Fanar 1 9B Instruct Fanar | 8.78B | Just fits | Q4_K_M | 7.07 GB | 33.6 t/s | 4k |
| Qwen3.5 9B Qwen | 9.65B | Just fits | IQ4_XS | 6.63 GB | 36.4 t/s | 8k |
| Mistral 7B Instruct V0.3 Mistral | 7.25B | Just fits | Q5_K_M | 6.65 GB | 36.2 t/s | 8k |
| Mistral 7B Instruct V0.2 Mistral | 7.24B | Just fits | Q5_K_M | 6.65 GB | 36.2 t/s | 8k |
| Gemma 2 9B Instruct Gemma | 9.24B | Just fits | IQ4_XS | 6.7 GB | 35.7 t/s | 8k |
| Qwen3 8B Qwen | 8.19B | Just fits | Q4_K_M | 6.58 GB | 36.6 t/s | 8k |
| Granite 3.0 8B Instruct Granite | 8.17B | Just fits | Q4_K_M | 6.69 GB | 35.9 t/s | 4k |
| T Lite Instruct 2.1 T-Lite | 8.19B | Just fits | Q4_K_M | 6.58 GB | 36.6 t/s | 8k |
| Gemma 3 12B Instruct Gemma | 12.19B | Just fits | Q3_K_M | 7.2 GB | 33 t/s | 8k |
| OLMo 3 7B Instruct OLMo | 7.3B | Just fits | Q4_K_M | 6.96 GB | 34.4 t/s | 8k |
| Gemma 4 E2B Instruct Gemma | 5.12B | Runs great | Q8_0 | 5.78 GB | 41.3 t/s | 128k |
| DeepSeek Coder V2 Lite Instruct DeepSeek | 15.71B (2.74B active) | Just fits | IQ3_XXS ! | 6.56 GB | 173 t/s | 16k |
| Qwen3.5 4B Qwen | 4.66B | Just fits | Q8_0 | 6.37 GB | 37.4 t/s | 8k |
| Agents A1 4B Other | 4.54B | Just fits | Q8_0 | 6.25 GB | 38.2 t/s | 8k |
| vLLM Translategemma 12B Instruct Gemma | 13.19B | Runs great | IQ3_XXS ! | 5.91 GB | 41.4 t/s | 32k |
| Gemma 3 4B Instruct Gemma | 4.3B | Runs great | Q8_0 | 5.31 GB | 46.1 t/s | 32k |
| Mistral Nemo Instruct 2407 Mistral | 12.25B | Just fits | IQ3_XXS ! | 6.53 GB | 37.4 t/s | 8k |
| Qwen3 4B Qwen | 4.02B | Runs great | Q8_0 | 5.86 GB | 41.1 t/s | 16k |
| Phi 3 Vision 128k Instruct Phi | 4.15B | Just fits | Q6_K | 6.96 GB | 34 t/s | 8k |
| Phi 4 Mini Instruct Phi | 3.84B | Runs great | Q8_0 | 5.59 GB | 43.7 t/s | 16k |
| Phi 3 Mini 4k Instruct Phi | 3.82B | Runs great | Q8_0 | 5.32 GB | 46.3 t/s | 4k |
| PowerLM 3B PowerLM | 3.51B | Just fits | Q8_0 | 7.03 GB | 33.4 t/s | 4k |
| Granite 4.1 3B Granite | 3.4B | Runs great | Q8_0 | 4.75 GB | 52.6 t/s | 32k |
| PowerMoE 3B PowerLM | 3.37B (0.88B active) | Runs great | Q8_0 | 4.53 GB | 153.1 t/s | 4k |
| Llama 3.2 3B Instruct Llama | 3.21B | Runs great | Q8_0 | 4.84 GB | 51.8 t/s | 16k |
| Qwen2.5 3B Instruct Qwen | 3.09B | Runs great | Q8_0 | 4.07 GB | 62.9 t/s | 32k |
| SmolLM3 3B Base SmolLM | 3.08B | Runs great | Q8_0 | 4.34 GB | 58.1 t/s | 32k |
| Starcoder2 3B StarCoder | 3.03B | Runs great | Q8_0 | 3.9 GB | 67.4 t/s | 16k |
| Phi 2 Phi | 2.78B | Runs great | Q8_0 | 6.01 GB | 40 t/s | 2k |
| DeepSeek Coder 7B Instruct V1.5 DeepSeek | 6.91B | Just fits | IQ3_XXS ! | 7.06 GB | 33.8 t/s | 4k |
| LFM2.5 2.6B Liquid | 2.7B | Runs great | Q8_0 | 3.87 GB | 66.8 t/s | 32k |
| Gemma 4 26B A4B Instruct Gemma | 25.81B | Just fits | IQ2_XXS ! | 7.2 GB | 32.7 t/s | 8k |
| Gemma 2 2B Instruct Gemma | 2.61B | Runs great | Q8_0 | 3.73 GB | 70.2 t/s | 8k |
| GPT OSS 20B GPT-OSS | 20.91B (4.18B active) | Runs great | IQ2_XXS ! | 5.8 GB | 208.2 t/s | 64k |
| Qwen3.5 2B Qwen | 2.27B | Runs great | Q8_0 | 3.35 GB | 80.1 t/s | 64k |
| OneRec 1.7B Other | 2.13B | Runs great | Q8_0 | 3.71 GB | 70.4 t/s | 32k |
| Qwen3 1.7B Qwen | 2.03B | Runs great | Q8_0 | 3.61 GB | 72.8 t/s | 32k |
| Qwen2.5 14B Instruct Qwen | 14.77B | Runs great | IQ2_XXS ! | 5.96 GB | 41.6 t/s | 8k |
| Qwen3 14B Qwen | 14.77B | Runs great | IQ2_XXS ! | 5.71 GB | 43.8 t/s | 8k |
| Phi 4 Phi | 14.66B | Runs great | IQ2_XXS ! | 5.99 GB | 41.3 t/s | 8k |
| Qwen1.5 MoE A2.7B Qwen | 14.32B (2.69B active) | Runs great | IQ2_XXS ! | 5.66 GB | 97.9 t/s | 8k |
| DeepSeek R1 Distill Qwen 1.5B Qwen | 1.78B | Runs great | Q8_0 | 2.57 GB | 112.2 t/s | 128k |
| Qwen3 1.7B Base Qwen | 1.72B | Runs great | Q8_0 | 3.3 GB | 81.5 t/s | 32k |
| SmolLM2 1.7B SmolLM | 1.71B | Runs great | Q8_0 | 3.92 GB | 65.8 t/s | 8k |
| Qwen2.5 1.5B Instruct Qwen | 1.54B | Runs great | Q8_0 | 2.44 GB | 120.5 t/s | 32k |
| Pythia 1.4B Pythia | 1.52B | Runs great | Q8_0 | 3.73 GB | 69.9 t/s | 2k |
| OLMo 2 0425 1B OLMo | 1.48B | Runs great | Q8_0 | 3.19 GB | 85.2 t/s | 4k |
| Qwen1.5 7B Qwen | 7.72B | Just fits | IQ2_XXS ! | 6.7 GB | 35.9 t/s | 8k |
| Falcon 7B Falcon | 7.22B | Just fits | IQ2_XXS ! | 7.05 GB | 34 t/s | 8k |
| Llama 3.2 1B Instruct Llama | 1.24B | Runs great | Q8_0 | 2.2 GB | 142.1 t/s | 64k |
| CodeLlama 7B Llama | 6.74B | Just fits | IQ2_XXS ! | 6.47 GB | 37.4 t/s | 8k |
| DeepSeek Coder 6.7B Instruct DeepSeek | 6.74B | Just fits | IQ2_XXS ! | 6.47 GB | 37.4 t/s | 8k |
| LFM2.5 1.2B Instruct Liquid | 1.17B | Runs great | Q8_0 | 2.13 GB | 149.1 t/s | 64k |
| TinyLlama 1.1B Chat V1.0 Llama | 1.1B | Runs great | Q8_0 | 1.99 GB | 166.6 t/s | 2k |
| MiniCPM5 1B MiniCPM | 1.08B | Runs great | Q8_0 | 1.95 GB | 167.1 t/s | 128k |
| Gemma 3 1B Instruct Gemma | 1B | Runs great | Q8_0 | 1.71 GB | 202.1 t/s | 32k |
| Qwen3.5 0.8B Qwen | 0.87B | Runs great | Q8_0 | 1.9 GB | 169.9 t/s | 64k |
| Sarashina2.2 0.5B Instruct V0.1 Sarashina | 0.79B | Runs great | Q8_0 | 1.93 GB | 167.9 t/s | 8k |
| Qwen3 0.6B Qwen | 0.75B | Runs great | Q8_0 | 2.28 GB | 129.8 t/s | 32k |
| Qwen1.5 0.5B Chat Qwen | 0.62B | Runs great | Q8_0 | 2.03 GB | 154 t/s | 32k |
| Qwen3 0.6B Base Qwen | 0.6B | Runs great | Q8_0 | 2.13 GB | 142.9 t/s | 32k |
| Pythia 410m Pythia | 0.51B | Runs great | Q8_0 | 1.92 GB | 167.3 t/s | 2k |
| H2o Danube3 500m Chat Danube | 0.51B | Runs great | Q8_0 | 1.57 GB | 238.6 t/s | 8k |
| Qwen2.5 0.5B Instruct Qwen | 0.49B | Runs great | Q8_0 | 1.23 GB | 362.8 t/s | 32k |
| SmolLM2 360M SmolLM | 0.36B | Runs great | Q8_0 | 1.33 GB | 313.9 t/s | 8k |
| LFM2.5 350M Liquid | 0.35B | Runs great | Q8_0 | 1.26 GB | 352 t/s | 64k |
| Pythia 160m Pythia | 0.21B | Runs great | Q8_0 | 1.14 GB | 429.2 t/s | 2k |
| Japanese GPT NeoX Small GPT-NeoX | 0.2B | Runs great | Q8_0 | 1.13 GB | 438.1 t/s | 2k |
| Llama 160m Llama | 0.16B | Runs great | Q8_0 | 1.09 GB | 477.6 t/s | 2k |
| LLM Jp 3 150m LLM-jp | 0.15B | Runs great | Q8_0 | 0.97 GB | 624.9 t/s | 4k |
| SmolLM2 135M SmolLM | 0.13B | Runs great | Q8_0 | 0.94 GB | 689.6 t/s | 8k |
| Pythia 70m Deduped Pythia | 0.1B | Runs great | Q8_0 | 0.82 GB | 1089.3 t/s | 2k |
| Qwen3.5 35B A3B Qwen | 35.95B (2.9B active) | Partial offload 12/40 layers | — | 21.57 GB | 18.8 t/s | — |
| GLM 4.7 Flash GLM | 31.22B (3.66B active) | Partial offload 16/47 layers | — | 18.69 GB | 18 t/s | — |
| Qwen3 30B A3B Qwen | 30.53B (3.34B active) | Partial offload 17/48 layers | — | 18.64 GB | 17.2 t/s | — |
| Qwen3 Next 80B A3B Instruct Qwen | 81.32B (3.19B active) | Partial offload 6/48 layers | — | 47.2 GB | 13.9 t/s | — |
| Qwen3 Coder Next Qwen | 79.67B (3.19B active) | Partial offload 6/48 layers | — | 46.27 GB | 13.9 t/s | — |
| GPT OSS 120B GPT-OSS | 116.83B (5.7B active) | Partial offload 3/36 layers | — | 66.48 GB | 10.6 t/s | — |
| Phi 3.5 MoE Instruct Phi | 41.87B (6.64B active) | Partial offload 8/32 layers | — | 25.39 GB | 8.5 t/s | — |
Showing the 90 best results of 133. The remaining 43 need more memory than this device has, at any quantisation.
What it will not run
7 of the 133 architectures we track are out of reach here, even at two-bit precision. Another 43 run by splitting layers between the card and system RAM, which works but drops generation to single digits.
If you want more headroom
The next steps up in memory, in order. More memory changes which models load at all; more bandwidth changes how fast they answer, so the two are worth weighing separately.
| Device | Memory | Bandwidth | Models that fit | |
|---|---|---|---|---|
| GeForce RTX 3080 10GB | 10 GB | 760 GB/s | 88 | Check price |
| Arc B570 | 10 GB | 380 GB/s | 88 | Check price |
| GeForce RTX 2080 Ti | 11 GB | 616 GB/s | 93 | Check price |
| GeForce GTX 1080 Ti | 11 GB | 484 GB/s | 93 | Check price |
Price links go to an Amazon search for the model name and are affiliate links: if you buy through one, we earn a commission at no cost to you. We do not take payment for placement, and the ordering above is by memory capacity alone.
Other devices with 8 GB
Same capacity, different speed. Once a model fits, bandwidth is what separates these.