Best GPU for local AI

Which AI models can the RTX 4080 Laptop run?

The RTX 4080 Laptop has 12 GB of graphics memory, read at 432 GB/s. In a laptop with 32 GB of DDR5-5600, 34 of the 88 open models here run entirely on the card, 34 of them writing faster than you read; the most capable it runs well is Qwen3 14B at Q4_K_M, 26–42 tokens/s. 29 more run with part of the model in system memory, more slowly.

Every figure is for an 8,192-token conversation (about 6,000 words), at the best compression that fits. Try it in the calculator with your own memory, conversation length and compression, or a second card. For image and video models, see Stable Diffusion, FLUX and Wan on the RTX 4080 Laptop.

Models that run entirely on the card

The fastest way to run a model: all of it in graphics memory. Most people read at 5 to 8 tokens a second, so anything above 10 feels quick.

Models the RTX 4080 Laptop runs entirely on the card
ModelCompressionWritesNeeds
Qwen3 14BQ4_K_M26–42 tokens/s10 GB
Phi-4Q4_K_M26–41 tokens/s11 GB
Ministral 3 14BQ4_K_M27–44 tokens/s9.8 GB
Gemma 3 12BQ5_K_M27–43 tokens/s9.5 GB
Gemma 4 12BQ6_K25–40 tokens/s10 GB
Qwen3.5 9BQ8_024–39 tokens/s11 GB
Ornith 1.5 9BQ8_024–39 tokens/s11 GB
Ministral 3 8BQ8_025–40 tokens/s11 GB
Granite 4.2 8BQ8_025–40 tokens/s11 GB
Qwen3 8BQ8_027–43 tokens/s9.9 GB
DeepSeek R1 0528 Qwen3 8BQ8_027–43 tokens/s9.9 GB
Llama 3.1 8BQ8_028–44 tokens/s9.6 GB
Gemma 4 E4BQ8_051–81 tokens/s8.7 GB
Qwen2.5 7BQ8_030–48 tokens/s8.6 GB
Mistral 7B v0.3Q8_030–49 tokens/s8.8 GB
Gemma 4 E2BQ8_096–155 tokens/s5.5 GB
Qwen3.5 4BQ8_049–78 tokens/s5.2 GB
Gemma 3 4BQ8_052–82 tokens/s4.9 GB
Qwen3 4B 2507Q8_051–81 tokens/s5.5 GB
Ministral 3 3BQ8_054–87 tokens/s5.0 GB
Phi-4-miniQ8_053–85 tokens/s5.2 GB
Granite 4.2 3BQ8_058–93 tokens/s4.6 GB
Llama 3.2 3BQ8_063–100 tokens/s4.4 GB
SmolLM3 3BQ8_068–110 tokens/s3.9 GB
Qwen3.5 2BQ8_096–155 tokens/s2.5 GB
Qwen3 1.7BQ8_091–145 tokens/s3.1 GB
LFM3 1.7BQ8_0120–195 tokens/s1.9 GB
Llama 3.2 1BQ8_0155–250 tokens/s1.6 GB
Gemma 3 1BQ8_0200–315 tokens/s1.1 GB
Qwen3.5 0.8BQ8_0215–350 tokens/s1.0 GB
Qwen3 0.6BQ8_0175–280 tokens/s1.7 GB
SmolLM2 360MQ8_0340–545 tokens/s0.7 GB
LFM2.5 350MQ8_0410–655 tokens/s0.5 GB
Gemma 3 270MQ8_0500–800 tokens/s0.3 GB

With help from system memory

In a laptop with 32 GB of DDR5-5600, these fit with part of the model in system memory. The processor reads that part several times more slowly, so they write more slowly the more of them is off the card.

Models the RTX 4080 Laptop runs with part in system memory
ModelOn the cardWritesNeeds
Qwen3.8 Flash Next30%36–58 tokens/s36 GB
Mistral Medium 3.5 128B38%2.2–3.5 tokens/s28 GB
Qwen3.5 122B-A10B31%18–29 tokens/s35 GB
Nemotron 3.5 Super 120B-A12B31%16–25 tokens/s34 GB
Mistral Small 4 119B33%27–43 tokens/s33 GB
GLM-4.5 Air 106B-A12B34%13–21 tokens/s32 GB
Llama 4 Scout 109B-A17B34%10.0–16 tokens/s31 GB
Qwen3 Coder Next 80B-A3B36%41–65 tokens/s30 GB
Nemotron 3.5 Bolt 74B-A9B29%12–19 tokens/s37 GB
Qwen2.5 72B36%2.0–3.2 tokens/s30 GB
Llama 3.3 70B37%2.1–3.4 tokens/s29 GB
Flint 40B-A2.5B44%34–54 tokens/s24 GB
Qwen3.6 35B-A3B49%32–51 tokens/s22 GB
Qwen3 32B75%8.1–13 tokens/s14 GB
Nemotron 3.5 Lightning 30B-A3B56%36–58 tokens/s19 GB
Gemma 4 31B84%11–17 tokens/s13 GB
GLM-4.7 Flash56%33–53 tokens/s19 GB
Qwen3 30B-A3B 250756%29–46 tokens/s19 GB
Qwen3 Coder 30B-A3B56%29–46 tokens/s19 GB
Muse Glimmer 30B95%18–28 tokens/s11 GB
Granite 4.2 30B82%11–17 tokens/s13 GB
Qwen3.6 27B77%8.1–13 tokens/s14 GB
Qwen3.8 27B77%8.1–13 tokens/s14 GB
Gemma 3 27B74%7.6–12 tokens/s15 GB
Gemma 4 26B-A4B68%33–52 tokens/s16 GB
Mistral Small 3.2 24B83%11–17 tokens/s13 GB
Devstral Small 2 24B83%11–17 tokens/s13 GB
LFM2.5 24B-A2B73%55–88 tokens/s15 GB
gpt-oss 20B88%57–91 tokens/s12 GB

The other 25 need more memory than a laptop with 32 GB of DDR5-5600 has; the calculator shows what more memory, or a second card, would add.

Compared with similar graphics

Tokens per second writing Llama 3.1 8B at 4 bits, which every 8 GB card holds, and the most capable model each runs well on its own. Every card ranked.

GraphicsMemoryLlama 3.1 8BMost capable it runs well
RTX 5080 Laptop16 GB110Qwen3 14B
RTX 5070 Ti Laptop12 GB83Qwen3 14B
RTX 4090 Laptop16 GB76Qwen3 14B
RTX 4080 Laptop12 GB61Qwen3 14B
RTX 5070 Laptop8 GB54Qwen3.5 9B
RTX 5060 Laptop8 GB52Qwen3.5 9B
RTX 3070 Laptop8 GB45Qwen3.5 9B

Estimated tokens per second; a real figure is usually within a quarter either way. * Part of it in system memory. {CAPABLE_NOTE}

How it is worked out

Writing an answer is limited by memory rather than arithmetic: for every word-piece (token) a model writes, the computer reads every weight that token uses once. So the speed is how fast the memory holding the model can be read, divided by how much is read for each token: the model’s weights at the chosen compression, plus the conversation so far. How much of its memory speed each graphics card reaches is measured, not assumed: it comes from the card’s own result in llama.cpp’s benchmark, which LM Studio, Ollama and Jan are built on. A model that does not fit in the graphics card’s memory spills into system memory, five to fifteen times slower, and one that does not fit in memory at all can be read from the drive as it runs, slower again; the estimate counts each part at its own speed, and a processor with few threads cannot use all of fast memory. Graphics built into a processor use system memory, at its speed. Reading a prompt is limited by arithmetic, so it follows the measured prompt speed.

Already running a model? See if it runs at full speed

A model that is slower than it should be is nearly always partly on the processor rather than all on the graphics card, which is several times slower — and Ollama does that without a word when something else holds the card’s memory. Bottleneck’s free app checks a model run with Ollama, LM Studio, llama.cpp, KoboldCpp or Jan: where it really runs, how fast it writes, and what your PC should manage with it. Nothing leaves your PC. Or check and fix it by hand.

It reads your parts too: the graphics card and how much memory it really has free, how much system memory is installed, the speed it runs at and in how many channels. Memory left in one channel, or below its rated speed, halves how fast a model runs from it, and Bottleneck says so. Its AI models screen then lists what runs on this PC.

The free version shows where a model runs and how fast, with no time limit and no account. Pro, $9 once, explains why it is slower than it could be and how to fix it. Compare the versions