Laptop GPU · NVIDIA

What AI models can a GeForce RTX 5090 Laptop run?

24 GB is the enthusiast standard, and it shows: a 32B model fits at a good quantization with a long context, and a 70B is reachable if you are willing to drop precision. At 896 GB/s there is enough bandwidth to keep generation responsive for anything that fits.

Memory24 GB
Bandwidth896 GB/s
Usable for a model23.2 GB
Runtime backendCUDA
ArchitectureBlackwell
Power175 W

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 5090 Laptop running Qwen3.5 27B at Q6_K

Wait for the first word288 ms
Then writes at31.6 tok/s
Whole answer5.8 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Where it sits

With 24 GB it belongs to the 24 GB tier. The 24 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 896 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 Q6_K 21.33 GB 31.6 t/s 32k
Gemma 4 31B Instruct
Gemma
31.27B Runs great Q4_K_M 21.31 GB 31.6 t/s 8k
Qwen3 32B
Qwen
32.76B Runs great Q4_K_M 20.42 GB 33 t/s 16k
Granite 4.1 30B
Granite
28.87B Runs great Q5_K_M 21.53 GB 31.2 t/s 8k
Gemma 3 27B Instruct
Gemma
27.43B Just fits Q6_K 22.48 GB 29.9 t/s 16k
Qwen2.5 32B Instruct
Qwen
32.76B Runs great Q4_K_M 20.42 GB 33 t/s 16k
Gemma 4 26B A4B Instruct
Gemma
25.81B (3.74B active) Runs great Q6_K 20.9 GB 104 t/s 32k
Qwen2.5 Coder 32B Instruct
Qwen
32.76B Runs great Q4_K_M 20.42 GB 33 t/s 16k
GLM 4.7 Flash
GLM
31.22B (3.66B active) Runs great Q5_K_M 21.22 GB 135.9 t/s 32k
Gemma 4 12B Instruct
Gemma
11.96B Runs great Q8_0 13.55 GB 50.1 t/s 64k
Mistral Small 24B Instruct 2501
Mistral
23.57B Runs great Q6_K 19.18 GB 35.2 t/s 32k
Qwen3 14B
Qwen
14.77B Runs great Q8_0 15.54 GB 43.7 t/s 32k
GPT OSS 20B
GPT-OSS
20.91B (4.18B active) Runs great MXFP4 11.55 GB 160.1 t/s 128k
Qwen3.5 35B A3B
Qwen
35.95B (2.9B active) Runs great Q4_K_M 20.48 GB 209.6 t/s 128k
Qwen3 30B A3B
Qwen
30.53B (3.34B active) Runs great Q5_K_M 21.11 GB 130.1 t/s 16k
Qwen2.5 14B Instruct
Qwen
14.77B Runs great Q8_0 15.79 GB 43 t/s 32k
Qwen2.5 Coder 14B Instruct
Qwen
14.77B Runs great Q8_0 15.79 GB 43 t/s 32k
Qwen3.5 9B
Qwen
9.65B Runs great Q8_0 9.25 GB 74.6 t/s 256k
Codestral 22B V0.1
Mistral
22.25B Runs great Q6_K 19.06 GB 35.5 t/s 16k
Phi 4
Phi
14.66B Runs great Q8_0 16 GB 42.4 t/s 16k
Qwen3 Coder 30B A3B Instruct
Qwen
30.53B (3.34B active) Runs great Q5_K_M 21.11 GB 130.1 t/s 16k
Gemma 3 12B Instruct
Gemma
12.19B Runs great Q8_0 13.27 GB 51.2 t/s 128k
Gemma 4 E4B Instruct
Gemma
8B Runs great Q8_0 8.42 GB 81.8 t/s 128k
Granite 4.1 8B
Granite
8.79B Runs great Q8_0 10.35 GB 66.3 t/s 64k
Phi 3.5 MoE Instruct
Phi
41.87B (6.64B active) Runs great IQ4_XS 22.06 GB 89.4 t/s 16k
Qwen3 8B
Qwen
8.19B Runs great Q8_0 9.02 GB 76.5 t/s 32k
Mistral Nemo Instruct 2407
Mistral
12.25B Runs great Q8_0 13.15 GB 51.9 t/s 64k
Qwen2.5 VL 7B Instruct
Qwen
8.29B Runs great Q8_0 8.49 GB 81.4 t/s 64k
Mixtral 8x7B Instruct V0.1
Mistral
46.7B (12.88B active) Just fits Q3_K_M 22.6 GB 55.8 t/s 8k
Qwen2.5 7B Instruct
Qwen
7.62B Runs great Q8_0 7.83 GB 88.6 t/s 32k
Show the other 56 models
Llama 3.1 8B Instruct 8.03B Runs great Q8_0 8.83 GB 78.2 t/s 64k
DeepSeek Coder V2 Lite Instruct 15.71B Runs great Q8_0 15.91 GB 131.3 t/s 128k
Gemma 2 9B Instruct 9.24B Runs great Q8_0 11.5 GB 59.3 t/s 8k
Qwen2.5 Coder 7B Instruct 7.62B Runs great Q8_0 7.83 GB 88.6 t/s 32k
OLMo 3 7B Instruct 7.3B Runs great Q8_0 9.72 GB 70.8 t/s 64k
Internlm3 8B Instruct 8.8B Runs great Q8_0 8.97 GB 77 t/s 32k
Mistral 7B Instruct V0.3 7.25B Runs great Q8_0 8.45 GB 82 t/s 32k
Gemma 4 E2B Instruct 5.12B Runs great Q8_0 5.44 GB 128.8 t/s 128k
Qwen3.5 4B 4.66B Runs great Q8_0 5.27 GB 134.3 t/s 256k
Mistral 7B Instruct V0.2 7.24B Runs great Q8_0 8.44 GB 82 t/s 32k
DeepSeek Coder 7B Instruct V1.5 6.91B Runs great Q8_0 10.58 GB 64.8 t/s 4k
DeepSeek Coder 6.7B Instruct 6.74B Runs great Q8_0 10.94 GB 62.6 t/s 16k
LFM2.5 8B A1B 8.47B Runs great Q8_0 8.81 GB 232.1 t/s 64k
Qwen3 4B 4.02B Runs great Q8_0 5.46 GB 129.2 t/s 32k
Gemma 3 4B Instruct 4.3B Runs great Q8_0 4.91 GB 144.8 t/s 128k
Llama 3.3 70B Instruct 70.55B Fits only at 2 to 3 bits IQ2_XXS 19.71 GB 34.4 t/s 16k
Qwen2.5 VL 72B Instruct 73.41B Fits only at 2 to 3 bits IQ2_XXS 20.35 GB 33.3 t/s 16k
Qwen2.5 72B Instruct 72.71B Fits only at 2 to 3 bits IQ2_XXS 20.18 GB 33.6 t/s 16k
Phi 4 Mini Instruct 3.84B Runs great Q8_0 5.17 GB 137.3 t/s 128k
Granite 4.1 3B 3.4B Runs great Q8_0 4.34 GB 165.2 t/s 128k
Qwen2.5 3B Instruct 3.09B Runs great Q8_0 3.68 GB 197.4 t/s 32k
Phi 3 Mini 4k Instruct 3.82B Runs great Q8_0 4.8 GB 148.7 t/s 4k
Llama 3.2 3B Instruct 3.21B Runs great Q8_0 4.42 GB 162.7 t/s 128k
LFM2.5 2.6B 2.7B Runs great Q8_0 3.14 GB 235.7 t/s 128k
Qwen3.5 2B 2.27B Runs great Q8_0 2.7 GB 279.6 t/s 256k
Gemma 2 2B Instruct 2.61B Runs great Q8_0 3.54 GB 206.5 t/s 8k
Starcoder2 3B 3.03B Runs great Q8_0 3.34 GB 222.3 t/s 16k
Qwen3 1.7B 2.03B Runs great Q8_0 3.22 GB 228.6 t/s 32k
DeepSeek R1 Distill Qwen 1.5B 1.78B Runs great Q8_0 2.07 GB 376.9 t/s 128k
Qwen2.5 1.5B Instruct 1.54B Runs great Q8_0 2.06 GB 378.3 t/s 32k
SmolLM2 1.7B 1.71B Runs great Q8_0 3.53 GB 206.5 t/s 8k
OLMo 2 0425 1B 1.48B Runs great Q8_0 2.6 GB 291.5 t/s 4k
Llama 3.2 1B Instruct 1.24B Runs great Q8_0 1.82 GB 446.3 t/s 128k
LFM2.5 1.2B Instruct 1.17B Runs great Q8_0 1.46 GB 589.3 t/s 64k
Gemma 3 1B Instruct 1B Runs great Q8_0 1.35 GB 634.6 t/s 32k
Qwen3.5 0.8B 0.87B Runs great Q8_0 1.28 GB 677.7 t/s 256k
Qwen3 0.6B 0.75B Runs great Q8_0 1.92 GB 407.6 t/s 32k
Qwen2.5 0.5B Instruct 0.49B Runs great Q8_0 0.88 GB 1139.3 t/s 32k
LFM2.5 350M 0.35B Runs great Q8_0 0.68 GB 1764.2 t/s 64k
Qwen3 Coder Next 79.67B Partial offload n/a 45.22 GB 55.1 t/s n/a
Qwen3 Next 80B A3B Instruct 81.32B Partial offload n/a 46.15 GB 53.4 t/s n/a
GPT OSS 120B 116.83B Partial offload n/a 59.4 GB 23.6 t/s n/a
Qwen3.5 122B A10B 125.09B Partial offload n/a 70.61 GB 17.4 t/s n/a
MiniMax M2.5 228.7B Partial offload n/a 106.13 GB 10.3 t/s n/a
MiniMax M2.7 228.69B Partial offload n/a 106.13 GB 10.3 t/s n/a
GLM 4.5 Air 110.47B Partial offload n/a 63.61 GB 10 t/s n/a
MiniMax M3 427.04B Partial offload n/a 240.84 GB 4 t/s n/a
Qwen3 235B A22B 235.09B Partial offload n/a 133.71 GB 4 t/s n/a
GLM 4.5 358.34B Partial offload n/a 204.37 GB 3 t/s n/a
DeepSeek V3.2 685.4B Partial offload n/a 385.91 GB 2.5 t/s n/a
DeepSeek R1 684.53B Partial offload n/a 385.42 GB 2.5 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
GLM 5.1 753.86B Partial offload n/a 424.51 GB 2.3 t/s n/a
GLM 5.2 753.33B Partial offload n/a 424.21 GB 2.2 t/s n/a
Llama 3 3 Nemotron Super 49B V1 49.87B Partial offload n/a 47.99 GB 1.2 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 17 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.

DeviceMemoryBandwidthModels that fit
GeForce RTX 5090 32 GB 1792 GB/s 69 Check price
Ryzen AI Max+ 395 32GB 32 GB 256 GB/s 55 Check price
Jetson AGX Orin 32GB 32 GB 204.8 GB/s 56 Check price
Intel Core Ultra 9 288V 32GB 32 GB 136 GB/s 53 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 24 GB

Same capacity, different speed. Once a model fits, bandwidth is what separates these.

Questions people ask about an RTX 5090 Laptop

How much VRAM does a GeForce RTX 5090 Laptop have?

The GeForce RTX 5090 Laptop has 24 GB of VRAM, of which roughly 23.2 GB is available to a model once the operating system takes its share.

Can a GeForce RTX 5090 Laptop run a 70B model?

Yes. At IQ2_XXS a 70B model fits in 19.71 GB and generates around 34.4 tokens per second.

What is the best local model for a GeForce RTX 5090 Laptop?

Qwen3.5 27B at Q6_K is the strongest all-round fit: 21.33 GB used, around 31.6 tokens per second at 8k context.