Best Local AI Models for NVIDIA RTX 5080 Laptop (16 GB VRAM)
The NVIDIA RTX 5080 Laptop has 16 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~32.0 GB system RAM assumed), ranked by popularity.
See also: Best GPU for running local LLMs.
The NVIDIA RTX 5080 Laptop is built on NVIDIA's Blackwell architecture featuring GDDR7 256-bit delivering 896 GB/s of raw memory bandwidth. The fastest 16 GB laptop GPU here: GDDR7 at 896 GB/s generates tokens about 55% faster than the RTX 4090 Laptop and 75% faster than the RTX 3080 Ti Laptop. Models up to about 14B–20B fit in VRAM in 4-bit quantization; power limit 80–150 W.
Equipped with 16 GB of dedicated VRAM, the NVIDIA RTX 5080 Laptop can run 34 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen2.5-32B-Instruct-GGUF, Qwen3-8B-GGUF, Qwen2.5-7B-Instruct-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 16 GB VRAM.
With a memory bandwidth of 896 GB/s, this card can generate tokens at an estimated peak rate of ~149.3 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 150W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.
How the NVIDIA RTX 5080 Laptop compares with similar cards
- Compared with the NVIDIA RTX 4090 Laptop, the NVIDIA RTX 5080 Laptop has the same 16 GB of VRAM, so the same models fit fully on the GPU; about 56% faster token generation (~149 vs ~96 tokens/s on an 8B model in Q4, from 896 vs 576 GB/s) and a newer architecture (Blackwell vs Ada Lovelace). Side-by-side comparison
- Compared with the NVIDIA RTX 3080 Ti Laptop, the NVIDIA RTX 5080 Laptop has the same 16 GB of VRAM, so the same models fit fully on the GPU; about 75% faster token generation (~149 vs ~85 tokens/s on an 8B model in Q4, from 896 vs 512 GB/s) and a newer architecture (Blackwell vs Ampere). Side-by-side comparison
- Compared with the NVIDIA RTX 5070 Ti Laptop, the NVIDIA RTX 5080 Laptop has 4 GB more VRAM (16 vs 12 GB), so larger models or longer contexts fit; about 33% faster token generation (~149 vs ~112 tokens/s on an 8B model in Q4, from 896 vs 672 GB/s) and a higher maximum power limit (150 vs 115 W). Side-by-side comparison
Token speeds here are a conservative estimate (memory bandwidth ÷ 6, for an 8B model in Q4 that reads about 5 GB of weights per token, plus overhead); prompt processing depends on GPU compute instead.
34 fit fully in VRAM · 4 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | IQ3_M | Fair |
15.59 GB
|
65.0 t/s | Fits in VRAM |
| Qwen/Qwen3-8B-GGUF | 8.19B | Q8_0 | Excellent |
9.47 GB
|
110.5 t/s | Fits in VRAM |
| Qwen/Qwen2.5-7B-Instruct-GGUF | 7.62B | GGUF | Excellent |
15.21 GB
|
63.1 t/s | Fits in VRAM |
| Qwen/Qwen3-4B-GGUF | 4.02B | Q8_0 | Excellent |
5.35 GB
|
224.8 t/s | Fits in VRAM |
| unsloth/gpt-oss-20b-GGUF | 20.91B | F16 | Very good |
13.74 GB
|
69.8 t/s | Fits in VRAM |
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q3_K_M | Fair |
14.88 GB
|
65.4 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-2.6B-GGUF | 2.7B | BF16 | Excellent |
5.89 GB
|
178.1 t/s | Fits in VRAM |
| Qwen/Qwen2.5-1.5B-Instruct-GGUF | 1.54B | GGUF | Excellent |
4.23 GB
|
270.2 t/s | Fits in VRAM |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.49B | GGUF | Excellent |
2.03 GB
|
759.7 t/s | Fits in VRAM |
| unsloth/Ornith-1.0-9B-GGUF | — | Q8_K_XL | Excellent |
13.02 GB
|
74.1 t/s | Fits in VRAM |
| bartowski/Meta-Llama-3.1-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent |
9.23 GB
|
112.6 t/s | Fits in VRAM |
| Qwen/Qwen3-14B-GGUF | 14.77B | Q6_K | Excellent |
12.71 GB
|
79.4 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-7B-Instruct-GGUF | 7.62B | GGUF | Excellent |
15.21 GB
|
63.1 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | GGUF | Excellent |
9.59 GB
|
109.0 t/s | Fits in VRAM |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | Q3_K_M | Fair |
15.99 GB
|
59.3 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-1.2B-Instruct-GGUF | 1.17B | BF16 | Excellent |
3.03 GB
|
410.6 t/s | Fits in VRAM |
| Qwen/Qwen2.5-3B-Instruct-GGUF | 3.09B | GGUF | Excellent |
7.27 GB
|
141.5 t/s | Fits in VRAM |
| ggml-org/gpt-oss-120b-GGUF | 116.83B | BF16 | Very low |
2.42 GB
|
606.3 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-230M-GGUF | 0.23B | BF16 | Excellent |
1.28 GB
|
2082.9 t/s | Fits in VRAM |
| unsloth/GLM-4.7-Flash-GGUF | 31.22B | Q3_K_M | Fair |
14.62 GB
|
65.8 t/s | Fits in VRAM |
| unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF | 8.0B | BF16 | Excellent |
14.87 GB
|
63.9 t/s | Fits in VRAM |
| unsloth/Qwen3-1.7B-GGUF | 1.72B | BF16 | Excellent |
4.45 GB
|
279.1 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF | 1.54B | Q8_0 | Excellent |
2.67 GB
|
507.8 t/s | Fits in VRAM |
| unsloth/flint-GGUF | 39.79B | IQ3_XXS | Low |
14.45 GB
|
66.2 t/s | Fits in VRAM |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | IQ3_S | Fair |
14.83 GB
|
64.2 t/s | Fits in VRAM |
| Qwen/Qwen3-0.6B-GGUF | 0.75B | Q8_0 | Excellent |
1.83 GB
|
1504.5 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-24B-A2B-GGUF | 23.97B | Q4_K_M | Good |
14.41 GB
|
66.2 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-14B-Instruct-GGUF | 14.77B | Q6_K | Excellent |
12.84 GB
|
79.3 t/s | Fits in VRAM |
| unsloth/Llama-3.2-3B-Instruct-GGUF | 3.21B | F16 | Excellent |
7.23 GB
|
149.5 t/s | Fits in VRAM |
| bartowski/Qwen2.5-14B-Instruct-GGUF | 14.77B | Q6_K_L | Excellent |
13.19 GB
|
77.0 t/s | Fits in VRAM |
| unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF | 8.19B | Q8_K_XL | Excellent |
11.44 GB
|
88.9 t/s | Fits in VRAM |
| bartowski/Llama-3.2-1B-Instruct-GGUF | 1.24B | F16 | Excellent |
3.3 GB
|
388.0 t/s | Fits in VRAM |
| bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF | 7.62B | F16 | Excellent |
15.21 GB
|
63.1 t/s | Fits in VRAM |
| LiquidAI/LFM3-1.7B-GGUF | 1.73B | BF16 | Excellent |
4.08 GB
|
277.5 t/s | Fits in VRAM |
| unsloth/Ornith-1.5-122B-GGUF | 125.09B | IQ3_XXS | Low |
47.01 GB
|
1.1 t/s | Offload |
| ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF | 31.58B | Q8_0 | Excellent |
32.1 GB
|
1.6 t/s | Offload |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | Q4_1 | Very good |
47.79 GB
|
1.1 t/s | Offload |
| google/gemma-7b | 8.54B | GGUF | Excellent |
33.11 GB
|
1.6 t/s | Offload |
"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate from the NVIDIA RTX 5080 Laptop's 896 GB/s memory bandwidth (offloaded models are limited by system RAM).
Frequently asked questions
What is the VRAM and memory bandwidth of the NVIDIA RTX 5080 Laptop?
The NVIDIA RTX 5080 Laptop features 16 GB of VRAM and a memory bandwidth of 896 GB/s (GDDR7 256-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.
What is the best local AI model to run on a NVIDIA RTX 5080 Laptop?
The best overall model for the NVIDIA RTX 5080 Laptop is bartowski/Qwen2.5-32B-Instruct-GGUF using the recommended IQ3_M quantization (15.59 GB total memory). With 16 GB of VRAM, this GPU typically runs a 14B at high quality, or a 24–27B at 4-bit at full GPU speed. Check our guide to the best LLMs for 16 GB VRAM for details.
Can the NVIDIA RTX 5080 Laptop run 14B and 32B models?
Yes. A 16 GB VRAM buffer allows you to run 14B models (like Qwen 2.5 14B or DeepSeek-R1 14B) in high-fidelity Q8_0 quantizations, or 32B models (like Qwen 2.5 32B) in Q3_K_M or Q4_K_M quantizations fully on the GPU.
NVIDIA RTX 5080 Laptop Head-to-Head Comparisons
Compare specs, memory bandwidth, and AI model capability against other graphics cards.