Laptop GPU · NVIDIA
What AI models can a GeForce RTX 5080 Laptop run?
16 GB covers almost everything people actually use day to day, including the 24B to 32B class that most reviewers consider the practical ceiling for a single consumer card. At 768 GB/s there is enough bandwidth to keep generation responsive for anything that fits.
The short answer
Assuming an 8k context window and default settings, these are the models worth downloading first.
Best all-rounder
Qwen3.5 27B
IQ4_XS · 14.2 GB · about 41.1 tokens/s
Best for code
Qwen2.5 Coder 14B Instruct
Q6_K · 12.63 GB · about 46.4 tokens/s
Largest that still runs well
GLM 4.7 Flash
31.22B parameters · Q3_K_M · about 158.9 tokens/s
Fastest useful answer
LFM2.5 8B A1B
about 198.9 tokens/s · 8.81 GB
See how fast it feels
GeForce RTX 5080 Laptop running Qwen3.5 27B at IQ4_XS
YouWhy does my model use more memory when the conversation gets longer?
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.
Where it sits
With 16 GB it belongs to the 16 GB tier. The 16 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 768 GB/s decides how fast it answers.
We do not track a price for this device, so it does not appear in the budget tools.
Every model, scored on this device
Each row uses the highest-quality quantization that both fits and stays conversational. Speed is a single-stream estimate at 8k context.
| Model | Params | Verdict | Download | Memory used | Speed | Max context |
|---|---|---|---|---|---|---|
| Qwen3.5 27B Qwen | 27.78B | Runs great | IQ4_XS | 14.2 GB | 41.1 t/s | 16k |
| Gemma 4 12B Instruct Gemma | 11.96B | Runs great | Q8_0 | 13.55 GB | 43 t/s | 16k |
| GPT OSS 20B GPT-OSS | 20.91B (4.18B active) | Runs great | MXFP4 | 11.55 GB | 137.2 t/s | 128k |
| Qwen3 14B Qwen | 14.77B | Runs great | Q6_K | 12.38 GB | 47.3 t/s | 16k |
| Gemma 3 27B Instruct Gemma | 27.43B | Just fits | IQ4_XS | 15.11 GB | 38.6 t/s | 8k |
| Qwen3.5 9B Qwen | 9.65B | Runs great | Q8_0 | 9.25 GB | 63.9 t/s | 128k |
| Qwen2.5 14B Instruct Qwen | 14.77B | Runs great | Q6_K | 12.63 GB | 46.4 t/s | 16k |
| Qwen2.5 Coder 14B Instruct Qwen | 14.77B | Runs great | Q6_K | 12.63 GB | 46.4 t/s | 16k |
| Gemma 4 26B A4B Instruct Gemma | 25.81B (3.74B active) | Runs great | IQ4_XS | 13.96 GB | 122.7 t/s | 16k |
| Phi 4 Phi | 14.66B | Runs great | Q6_K | 12.81 GB | 45.7 t/s | 16k |
| Mistral Small 24B Instruct 2501 Mistral | 23.57B | Just fits | Q4_K_M | 14.57 GB | 40 t/s | 8k |
| Gemma 3 12B Instruct Gemma | 12.19B | Runs great | Q8_0 | 13.27 GB | 43.9 t/s | 32k |
| Gemma 4 31B Instruct Gemma | 31.27B | Fits only at 2 to 3 bits | IQ3_XXS ! | 14.87 GB | 39.2 t/s | 8k |
| Gemma 4 E4B Instruct Gemma | 8B | Runs great | Q8_0 | 8.42 GB | 70.1 t/s | 128k |
| Granite 4.1 8B Granite | 8.79B | Runs great | Q8_0 | 10.35 GB | 56.8 t/s | 32k |
| Qwen3 8B Qwen | 8.19B | Runs great | Q8_0 | 9.02 GB | 65.6 t/s | 32k |
| Codestral 22B V0.1 Mistral | 22.25B | Just fits | Q4_K_M | 14.62 GB | 39.9 t/s | 8k |
| Mistral Nemo Instruct 2407 Mistral | 12.25B | Runs great | Q8_0 | 13.15 GB | 44.5 t/s | 16k |
| Qwen3 32B Qwen | 32.76B | Fits only at 2 to 3 bits | IQ3_XXS ! | 13.83 GB | 42.2 t/s | 8k |
| Qwen2.5 VL 7B Instruct Qwen | 8.29B | Runs great | Q8_0 | 8.49 GB | 69.7 t/s | 64k |
| GLM 4.7 Flash GLM | 31.22B (3.66B active) | Just fits | Q3_K_M | 14.82 GB | 158.9 t/s | 8k |
| Qwen2.5 7B Instruct Qwen | 7.62B | Runs great | Q8_0 | 7.83 GB | 76 t/s | 32k |
| Llama 3.1 8B Instruct Llama | 8.03B | Runs great | Q8_0 | 8.83 GB | 67.1 t/s | 32k |
| Granite 4.1 30B Granite | 28.87B | Fits only at 2 to 3 bits | IQ3_XXS ! | 12.69 GB | 46 t/s | 16k |
| Qwen3 30B A3B Qwen | 30.53B (3.34B active) | Just fits | Q3_K_M | 14.84 GB | 145.3 t/s | 8k |
| Qwen2.5 32B Instruct Qwen | 32.76B | Fits only at 2 to 3 bits | IQ3_XXS ! | 13.83 GB | 42.2 t/s | 8k |
| Gemma 2 9B Instruct Gemma | 9.24B | Runs great | Q8_0 | 11.5 GB | 50.9 t/s | 8k |
| Qwen2.5 Coder 32B Instruct Qwen | 32.76B | Fits only at 2 to 3 bits | IQ3_XXS ! | 13.83 GB | 42.2 t/s | 8k |
| DeepSeek Coder V2 Lite Instruct DeepSeek | 15.71B (2.74B active) | Runs great | Q6_K | 12.41 GB | 142.4 t/s | 64k |
| Qwen2.5 Coder 7B Instruct Qwen | 7.62B | Runs great | Q8_0 | 7.83 GB | 76 t/s | 32k |
Show the other 56 models
| Qwen3 Coder 30B A3B Instruct | 30.53B | Just fits | Q3_K_M | 14.84 GB | 145.3 t/s | 8k |
| OLMo 3 7B Instruct | 7.3B | Runs great | Q8_0 | 9.72 GB | 60.6 t/s | 32k |
| Internlm3 8B Instruct | 8.8B | Runs great | Q8_0 | 8.97 GB | 66 t/s | 32k |
| Mistral 7B Instruct V0.3 | 7.25B | Runs great | Q8_0 | 8.45 GB | 70.3 t/s | 32k |
| Gemma 4 E2B Instruct | 5.12B | Runs great | Q8_0 | 5.44 GB | 110.4 t/s | 128k |
| Qwen3.5 4B | 4.66B | Runs great | Q8_0 | 5.27 GB | 115.1 t/s | 256k |
| Mistral 7B Instruct V0.2 | 7.24B | Runs great | Q8_0 | 8.44 GB | 70.3 t/s | 32k |
| DeepSeek Coder 7B Instruct V1.5 | 6.91B | Runs great | Q8_0 | 10.58 GB | 55.5 t/s | 4k |
| DeepSeek Coder 6.7B Instruct | 6.74B | Runs great | Q8_0 | 10.94 GB | 53.6 t/s | 16k |
| LFM2.5 8B A1B | 8.47B | Runs great | Q8_0 | 8.81 GB | 198.9 t/s | 64k |
| Qwen3 4B | 4.02B | Runs great | Q8_0 | 5.46 GB | 110.7 t/s | 32k |
| Gemma 3 4B Instruct | 4.3B | Runs great | Q8_0 | 4.91 GB | 124.1 t/s | 128k |
| Phi 4 Mini Instruct | 3.84B | Runs great | Q8_0 | 5.17 GB | 117.7 t/s | 64k |
| Granite 4.1 3B | 3.4B | Runs great | Q8_0 | 4.34 GB | 141.6 t/s | 128k |
| Qwen2.5 3B Instruct | 3.09B | Runs great | Q8_0 | 3.68 GB | 169.2 t/s | 32k |
| Phi 3 Mini 4k Instruct | 3.82B | Runs great | Q8_0 | 4.8 GB | 127.5 t/s | 4k |
| Llama 3.2 3B Instruct | 3.21B | Runs great | Q8_0 | 4.42 GB | 139.5 t/s | 64k |
| LFM2.5 2.6B | 2.7B | Runs great | Q8_0 | 3.14 GB | 202 t/s | 128k |
| Qwen3.5 2B | 2.27B | Runs great | Q8_0 | 2.7 GB | 239.6 t/s | 256k |
| Gemma 2 2B Instruct | 2.61B | Runs great | Q8_0 | 3.54 GB | 177 t/s | 8k |
| Starcoder2 3B | 3.03B | Runs great | Q8_0 | 3.34 GB | 190.5 t/s | 16k |
| Qwen3 1.7B | 2.03B | Runs great | Q8_0 | 3.22 GB | 195.9 t/s | 32k |
| DeepSeek R1 Distill Qwen 1.5B | 1.78B | Runs great | Q8_0 | 2.07 GB | 323 t/s | 128k |
| Qwen2.5 1.5B Instruct | 1.54B | Runs great | Q8_0 | 2.06 GB | 324.3 t/s | 32k |
| SmolLM2 1.7B | 1.71B | Runs great | Q8_0 | 3.53 GB | 177 t/s | 8k |
| OLMo 2 0425 1B | 1.48B | Runs great | Q8_0 | 2.6 GB | 249.9 t/s | 4k |
| Llama 3.2 1B Instruct | 1.24B | Runs great | Q8_0 | 1.82 GB | 382.6 t/s | 128k |
| LFM2.5 1.2B Instruct | 1.17B | Runs great | Q8_0 | 1.46 GB | 505.1 t/s | 64k |
| Gemma 3 1B Instruct | 1B | Runs great | Q8_0 | 1.35 GB | 543.9 t/s | 32k |
| Qwen3.5 0.8B | 0.87B | Runs great | Q8_0 | 1.28 GB | 580.8 t/s | 256k |
| Qwen3 0.6B | 0.75B | Runs great | Q8_0 | 1.92 GB | 349.4 t/s | 32k |
| Qwen2.5 0.5B Instruct | 0.49B | Runs great | Q8_0 | 0.88 GB | 976.6 t/s | 32k |
| LFM2.5 350M | 0.35B | Runs great | Q8_0 | 0.68 GB | 1512.1 t/s | 64k |
| Qwen3.5 35B A3B | 35.95B | Partial offload | n/a | 20.48 GB | 102.3 t/s | n/a |
| Qwen3 Coder Next | 79.67B | Partial offload | n/a | 45.22 GB | 42.5 t/s | n/a |
| Qwen3 Next 80B A3B Instruct | 81.32B | Partial offload | n/a | 46.15 GB | 41.5 t/s | n/a |
| GPT OSS 120B | 116.83B | Partial offload | n/a | 59.4 GB | 19.7 t/s | n/a |
| Phi 3.5 MoE Instruct | 41.87B | Partial offload | n/a | 24.87 GB | 19.6 t/s | n/a |
| Qwen3.5 122B A10B | 125.09B | Partial offload | n/a | 70.61 GB | 15.1 t/s | n/a |
| GLM 4.5 Air | 110.47B | Partial offload | n/a | 51.85 GB | 10.9 t/s | n/a |
| Mixtral 8x7B Instruct V0.1 | 46.7B | Partial offload | n/a | 24.45 GB | 10.8 t/s | n/a |
| MiniMax M2.5 | 228.7B | Partial offload | n/a | 130.56 GB | 7.5 t/s | n/a |
| MiniMax M2.7 | 228.69B | Partial offload | n/a | 130.55 GB | 7.5 t/s | n/a |
| MiniMax M3 | 427.04B | Partial offload | n/a | 240.84 GB | 3.9 t/s | n/a |
| Qwen3 235B A22B | 235.09B | Partial offload | n/a | 133.71 GB | 3.8 t/s | n/a |
| GLM 4.5 | 358.34B | Partial offload | n/a | 204.37 GB | 2.8 t/s | n/a |
| Kimi K2.6 | 1026.88B | Partial offload | n/a | 577.78 GB | 2.4 t/s | n/a |
| Kimi K2 Instruct | 1026.41B | Partial offload | n/a | 577.52 GB | 2.4 t/s | n/a |
| DeepSeek V3.2 | 685.4B | Partial offload | n/a | 385.91 GB | 2.4 t/s | n/a |
| DeepSeek R1 | 684.53B | Partial offload | n/a | 385.42 GB | 2.4 t/s | n/a |
| GLM 5.1 | 753.86B | Partial offload | n/a | 424.51 GB | 2.2 t/s | n/a |
| Llama 3.3 70B Instruct | 70.55B | Partial offload | n/a | 42.12 GB | 1.1 t/s | n/a |
| GLM 5.2 | 753.33B | Partial offload | n/a | 424.21 GB | 1 t/s | n/a |
| Qwen2.5 VL 72B Instruct | 73.41B | Partial offload | n/a | 43.62 GB | 1 t/s | n/a |
| Qwen2.5 72B Instruct | 72.71B | Partial offload | n/a | 43.22 GB | 1 t/s | n/a |
| Llama 3 3 Nemotron Super 49B V1 | 49.87B | Partial offload | n/a | 47.99 GB | 0.9 t/s | n/a |
What it will not run
0 of the 86 architectures we track are out of reach here, even at 2-bit precision. Another 23 run by splitting the model between the card and system RAM, which works but drops generation to single digits for most dense models.
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 | |
|---|---|---|---|---|
| Radeon RX 7900 XT | 20 GB | 800 GB/s | 64 | Check price |
| GeForce RTX 4090 | 24 GB | 1008 GB/s | 69 | Check price |
| GeForce RTX 3090 Ti | 24 GB | 1008 GB/s | 69 | Check price |
| Radeon RX 7900 XTX | 24 GB | 960 GB/s | 69 | 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 16 GB
Same capacity, different speed. Once a model fits, bandwidth is what separates these.
Questions people ask about an RTX 5080 Laptop
How much VRAM does a GeForce RTX 5080 Laptop have?
The GeForce RTX 5080 Laptop has 16 GB of VRAM, of which roughly 15.2 GB is available to a model once the operating system takes its share.
Can a GeForce RTX 5080 Laptop run a 70B model?
Only partially. About 48 of 48 layers fit on the card and the rest run on the CPU, which brings generation down to roughly 42.5 tokens per second.
What is the best local model for a GeForce RTX 5080 Laptop?
Qwen3.5 27B at IQ4_XS is the strongest all-round fit: 14.2 GB used, around 41.1 tokens per second at 8k context.