Best Local AI Models for NVIDIA RTX 4080 Laptop (12 GB VRAM)

Q: What is the VRAM and memory bandwidth of the NVIDIA RTX 4080 Laptop?

The NVIDIA RTX 4080 Laptop features 12 GB of VRAM and a memory bandwidth of 432 GB/s (GDDR6 192-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.

Q: What is the best local AI model to run on a NVIDIA RTX 4080 Laptop?

The best overall model for the NVIDIA RTX 4080 Laptop is bartowski/Qwen2.5-32B-Instruct-GGUF using the recommended IQ2_S quantization (11.47 GB total memory). With 12 GB of VRAM, this GPU typically runs a 14B model at Q5, comfortably at full GPU speed. Check our guide to the best LLMs for 12 GB VRAM for details.

The NVIDIA RTX 4080 Laptop has 12 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~16.0 GB system RAM assumed), ranked by popularity.

Verdict: With 12 GB of GDDR6, 7B–12B models fit entirely in VRAM in 4-bit quantization, 14B is borderline once the context grows, and larger models spill into system RAM. Each laptop maker sets the power limit (60–150 W).

VRAM & Memory

12 GB

GDDR6 192-bit

Bandwidth

432 GB/s

Ada Lovelace

Fits in VRAM

34 models

Zero offload

TDP / Assumed RAM

150W

RAM: 16.0 GB

At a glance — 34 of the 36 most popular models run fully in VRAM on the NVIDIA RTX 4080 Laptop (12 GB), with about 72 tokens/s estimated on an 8B model in Q4. A solid pick is bartowski/Qwen2.5-32B-Instruct-GGUF (IQ2_S, 11.47 GB, ~44.7 t/s estimated).

The NVIDIA RTX 4080 Laptop is built on NVIDIA's Ada Lovelace architecture featuring GDDR6 192-bit delivering 432 GB/s of raw memory bandwidth. With 12 GB of GDDR6, 7B–12B models fit entirely in VRAM in 4-bit quantization, 14B is borderline once the context grows, and larger models spill into system RAM. Each laptop maker sets the power limit (60–150 W).

Equipped with 12 GB of dedicated VRAM, the NVIDIA RTX 4080 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 12 GB VRAM.

With a memory bandwidth of 432 GB/s, this card can generate tokens at an estimated peak rate of ~72.0 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.

New to this? Read: How much VRAM do you need?

How the NVIDIA RTX 4080 Laptop compares with similar cards

Compared with the NVIDIA RTX 5070 Ti Laptop, the NVIDIA RTX 4080 Laptop has the same 12 GB of VRAM, so the same models fit fully on the GPU; about 36% slower token generation (~72 vs ~112 tokens/s on an 8B model in Q4, from 432 vs 672 GB/s); a higher maximum power limit (150 vs 115 W) and an older architecture (Ada Lovelace vs Blackwell). Side-by-side comparison
Compared with the NVIDIA RTX 3070 Ti / 3070 Laptop, the NVIDIA RTX 4080 Laptop has 4 GB more VRAM (12 vs 8 GB), so larger models or longer contexts fit; about 4% slower token generation (~72 vs ~75 tokens/s on an 8B model in Q4, from 432 vs 448 GB/s); a higher maximum power limit (150 vs 125 W) and a newer architecture (Ada Lovelace vs Ampere). Side-by-side comparison
Compared with the NVIDIA RTX 5070/5060/5050 Laptop, the NVIDIA RTX 4080 Laptop has 4 GB more VRAM (12 vs 8 GB), so larger models or longer contexts fit; about 12% faster token generation (~72 vs ~64 tokens/s on an 8B model in Q4, from 432 vs 384 GB/s); a higher maximum power limit (150 vs 100 W) and an older architecture (Ada Lovelace vs Blackwell). 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 · 2 run with offload

Only show models that fit fully in VRAM

Model	Size	Quant.	Quality	Memory	Speed~	Verdict
bartowski/Qwen2.5-32B-Instruct-GGUF	32.76B	IQ2_S	Very low	11.47 GB	44.7 t/s	Fits in VRAM
Qwen/Qwen3-8B-GGUF	8.19B	Q8_0	Excellent	9.47 GB	53.3 t/s	Fits in VRAM
Qwen/Qwen2.5-7B-Instruct-GGUF	7.62B	Q8_0	Excellent	8.56 GB	57.3 t/s	Fits in VRAM
Qwen/Qwen3-4B-GGUF	4.02B	Q8_0	Excellent	5.35 GB	108.4 t/s	Fits in VRAM
unsloth/gpt-oss-20b-GGUF	20.91B	Q4_K_XL	Good	11.95 GB	39.1 t/s	Fits in VRAM
unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF	30.53B	Q2_K_L	Low	11.73 GB	40.9 t/s	Fits in VRAM
Qwen/Qwen2.5-1.5B-Instruct-GGUF	1.54B	GGUF	Excellent	4.23 GB	130.3 t/s	Fits in VRAM
LiquidAI/LFM2.5-2.6B-GGUF	2.7B	BF16	Excellent	5.89 GB	85.8 t/s	Fits in VRAM
Qwen/Qwen2.5-0.5B-Instruct-GGUF	0.49B	GGUF	Excellent	2.03 GB	366.3 t/s	Fits in VRAM
unsloth/Ornith-1.0-9B-GGUF	—	Q8_0	Excellent	9.8 GB	48.7 t/s	Fits in VRAM
bartowski/Meta-Llama-3.1-8B-Instruct-GGUF	8.03B	Q8_0	Excellent	9.23 GB	54.3 t/s	Fits in VRAM
Qwen/Qwen3-14B-GGUF	14.77B	Q5_K_M	Very good	11.22 GB	44.1 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-7B-Instruct-GGUF	7.62B	Q8_0	Excellent	8.56 GB	57.3 t/s	Fits in VRAM
janhq/Jan-v3.5-4B-gguf	4.41B	GGUF	Excellent	9.59 GB	52.5 t/s	Fits in VRAM
bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF	34.66B	IQ2_S	Very low	11.13 GB	42.1 t/s	Fits in VRAM
unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF	8.0B	Q8_K_XL	Excellent	8.48 GB	56.6 t/s	Fits in VRAM
LiquidAI/LFM2.5-1.2B-Instruct-GGUF	1.17B	BF16	Excellent	3.03 GB	197.9 t/s	Fits in VRAM
Qwen/Qwen2.5-3B-Instruct-GGUF	3.09B	GGUF	Excellent	7.27 GB	68.2 t/s	Fits in VRAM
unsloth/GLM-4.7-Flash-GGUF	31.22B	Q2_K_L	Low	11.64 GB	40.6 t/s	Fits in VRAM
unsloth/Qwen3-1.7B-GGUF	1.72B	BF16	Excellent	4.45 GB	134.6 t/s	Fits in VRAM
unsloth/LFM2.5-24B-A2B-GGUF	23.97B	Q3_K_XL	Fair	11.07 GB	42.4 t/s	Fits in VRAM
unsloth/flint-GGUF	39.79B	IQ2_XXS	Very low	11.95 GB	39.1 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF	1.54B	Q8_0	Excellent	2.67 GB	244.8 t/s	Fits in VRAM
LiquidAI/LFM2.5-230M-GGUF	0.23B	BF16	Excellent	1.28 GB	1004.3 t/s	Fits in VRAM
ggml-org/gpt-oss-120b-GGUF	116.83B	BF16	Very low	2.42 GB	292.3 t/s	Fits in VRAM
Qwen/Qwen3-0.6B-GGUF	0.75B	Q8_0	Excellent	1.83 GB	725.4 t/s	Fits in VRAM
unsloth/Qwen-AgentWorld-35B-A3B-GGUF	34.66B	IQ2_M	Low	11.65 GB	40.1 t/s	Fits in VRAM
unsloth/Llama-3.2-3B-Instruct-GGUF	3.21B	F16	Excellent	7.23 GB	72.1 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-14B-Instruct-GGUF	14.77B	Q5_K_M	Very good	11.34 GB	44.1 t/s	Fits in VRAM
bartowski/Qwen2.5-14B-Instruct-GGUF	14.77B	Q5_K_L	Very good	11.78 GB	42.2 t/s	Fits in VRAM
unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF	8.19B	Q8_K_XL	Excellent	11.44 GB	42.9 t/s	Fits in VRAM
bartowski/Llama-3.2-1B-Instruct-GGUF	1.24B	F16	Excellent	3.3 GB	187.1 t/s	Fits in VRAM
LiquidAI/LFM3-1.7B-GGUF	1.73B	BF16	Excellent	4.08 GB	133.8 t/s	Fits in VRAM
bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF	7.62B	Q8_0	Excellent	8.56 GB	57.3 t/s	Fits in VRAM
ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF	31.58B	Q4_0	Good	18.42 GB	2.8 t/s	Offload
unsloth/Qwen3-Coder-Next-GGUF	79.67B	IQ3_XXS	Low	27.7 GB	1.9 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 4080 Laptop's 432 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 4080 Laptop?

The NVIDIA RTX 4080 Laptop features 12 GB of VRAM and a memory bandwidth of 432 GB/s (GDDR6 192-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 4080 Laptop?

The best overall model for the NVIDIA RTX 4080 Laptop is bartowski/Qwen2.5-32B-Instruct-GGUF using the recommended IQ2_S quantization (11.47 GB total memory). With 12 GB of VRAM, this GPU typically runs a 14B model at Q5, comfortably at full GPU speed. Check our guide to the best LLMs for 12 GB VRAM for details.

Can the NVIDIA RTX 4080 Laptop run 14B models locally?

Yes. The NVIDIA RTX 4080 Laptop's 12 GB VRAM is the exact sweet spot for running 14B models (such as Qwen 2.5 Coder 14B) in Q4_K_M quantization (~9.2 GB footprint) with full 4,096-token context entirely in VRAM.