NVIDIA · Blackwell · 16 GB VRAM tier

Best AI Models for NVIDIA GeForce RTX 5080 Laptop GPU (16 GB)

Runs 14B models well; 27–31B only just fits.

16 GB GDDR7896 GB/s70 TFLOPS FP16150 WBlackwell 2025

Laptop GPU has 16 GB (same as the desktop 5080) but much lower power; fits ~14B Q4 / ~24B at tight quants. Speed depends on the chassis power limit.

What LLMs can the RTX 5080 Laptop GPU run?

#	Model	Speed	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 91.6 t/s	91.6 tok/s	Q4_K_M · 6.6 GB
2	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 179 t/s	179 tok/s	Q4_K_M · 3.4 GB
3	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 173 t/s	173 tok/s	Q4_K_M · 13.1 GB
4	Phi 4Microsoft Q4_K_M · 12.5 GB · 61.2 t/s	61.2 tok/s	Q4_K_M · 12.5 GB
5	Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s	70.8 tok/s	Q4_K_M · 9.0 GB

#	Model	Speed	Coding Score	Quant · VRAM
1	GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 173 t/s	173 tok/s		Q4_K_M · 13.1 GB
2	Llama 3.1 8B InstructMeta Q4_K_M · 7.9 GB · 110 t/s	110 tok/s		Q4_K_M · 7.9 GB
3	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 91.6 t/s	91.6 tok/s	overall	Q4_K_M · 6.6 GB
4	Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s	70.8 tok/s		Q4_K_M · 9.0 GB
5	MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M · 6.5 GB · 93.8 t/s	93.8 tok/s		Q4_K_M · 6.5 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 91.6 t/s	91.6 tok/s	Q4_K_M · 6.6 GB
2	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 173 t/s	173 tok/s	Q4_K_M · 13.1 GB
3	Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 106 t/s	106 tok/s	Q4_K_M · 7.6 GB
4	Llama 3.1 8B InstructMeta Q4_K_M · 7.9 GB · 110 t/s	110 tok/s	Q4_K_M · 7.9 GB
5	Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s	70.8 tok/s	Q4_K_M · 9.0 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 91.6 t/s	91.6 tok/s	Q4_K_M · 6.6 GB
2	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 179 t/s	179 tok/s	Q4_K_M · 3.4 GB
3	Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 72.2 t/s	72.2 tok/s	Q4_K_M · 11.0 GB
4	Llama 3.1 8B InstructMeta Q4_K_M · 7.9 GB · 110 t/s	110 tok/s	Q4_K_M · 7.9 GB
5	Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s	70.8 tok/s	Q4_K_M · 9.0 GB

#	Model	Speed ↓	Quant · VRAM
1	Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 179 t/s	179 tok/s	Q4_K_M · 3.4 GB
2	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 173 t/s	173 tok/s	Q4_K_M · 13.1 GB
3	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 91.6 t/s	91.6 tok/s	Q4_K_M · 6.6 GB
4	Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 70.8 t/s	70.8 tok/s	Q4_K_M · 9.0 GB
5	Phi 4Microsoft Q4_K_M · 12.5 GB · 61.2 t/s	61.2 tok/s	Q4_K_M · 12.5 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 91.6 t/s	91.6 tok/s	Q4_K_M · 6.6 GB
2	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 179 t/s	179 tok/s	Q4_K_M · 3.4 GB
3	Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 72.4 t/s	72.4 tok/s	Q4_K_M · 10.9 GB
4	Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 205 t/s	205 tok/s	Q4_K_M · 5.0 GB
5	MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M · 6.5 GB · 93.8 t/s	93.8 tok/s	Q4_K_M · 6.5 GB

See all 309 models on the RTX 5080 Laptop GPU →Best quant that fits · speeds estimated on this card

About the RTX 5080 Laptop GPU

The RTX 5080 Laptop GPU runs 14B models well. Blackwell generation, released 2025.

GenerationBlackwellreleased 2025

RTX 5080 Laptop GPU strengths and weaknesses

Strengths

Runs 35B MoE models: Qwen3.6 35B A3B at 174 tok/s in 15.7 GB
Low power draw: 150 W board power; a 300 W PSU is enough

Weaknesses

70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 16 GB here

Similar GPUs

Cards people weigh against the RTX 5080 Laptop GPU, each with the one reason it is worth a look.

4 GB more for bigger modelsRX 7900 XTAMD · 20 GB · RDNA 3

▲ 4 GB$89975.5 tok/s

8 GB more for bigger modelsRX 7900 XTXAMD · 24 GB · RDNA 3

▲ 8 GB$99990.6 tok/s

8 GB more for bigger modelsRTX 5090 Laptop GPUNVIDIA · 24 GB · Blackwell

▲ 8 GB91.6 tok/s

8 GB more for bigger modelsRTX 3090NVIDIA · 24 GB · Ampere

▲ 8 GB$1,499rent $0.18/hr95.7 tok/s

Best value near the RTX 5080 Laptop GPURTX 5080NVIDIA · 16 GB · Blackwell

same 16 GB$2,394rent $0.24/hr98.1 tok/s

8 GB more for bigger modelsRTX 3090 TiNVIDIA · 24 GB · Ampere

▲ 8 GB$1,999rent $0.35/hr103 tok/s

RTX 5080 Laptop GPU tokens per second by model size

Output speed depends on memory bandwidth (896 GB/s). Grey rows need more than 16 GB.

9BQwen3.5 9B · Q4_K_M · 6.6 GB
91.6tok/s
14BQwen3 14B · Q4_K_M · 11.9 GB
61.3tok/s
27–31BQwen3.8 27B · Q3_K_L · 15.4 GB tight · Q3_K_L
38.9tok/s
35B MoEQwen3.6 35B A3B · IQ3_S · 15.7 GB tight · IQ3_S
174tok/s
70BLlama 3.3 70B Instruct · not at 4-bit
needs 51.3 GB
120B MoEGPT OSS 120B · not at 4-bit
needs 70.8 GB

Estimated speeds: Q4_K_M, 4K context, one user. How we estimate

All 1073 models on the RTX 5080 Laptop GPU

Featured only

Best quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works

LLM models compatible with NVIDIA GeForce RTX 5080 Laptop GPU — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Ouro Hybrid 1.4B1.5BChat BF16·156.1 t/s	BF16	6.5 GB41%	156.1 t/s	66K	S97, Runs great
Gemma 4 26B A4B IT Assistant26BChat Q4_K_S·240.0 t/s	Q4_K_S	15.1 GB94%	240.0 t/s	262K	B62, Decent
Qwen3.5 9B Abliterated9.0BChatFunctions Q4_K_M·98.0 t/s	Q4_K_M	6.2 GB39%	98.0 t/s	262K	S91, Runs great
Kimi K2.6 Eagle31.8BChat Q4_K_M·398.9 t/s	Q4_K_M	1.9 GB12%	398.9 t/s	262K	S100, Runs great
JiRackDeltaNet 27B27.3BChatFunctions Q3_K_M·41.4 t/s	Q3_K_M	14.5 GB91%	41.4 t/s	262K	B60, Decent
NVIDIA Nemotron Parse 2.0903MVision BF16·292.7 t/s	BF16	3.4 GB21%	292.7 t/s	—	S100, Runs great
Kimi Linear Tiny Random6MChatFunctions BF16·1359.5 t/s	BF16	0.3 GB2%	1359.5 t/s	—	S100, Runs great
SmolLM 135M Instruct FP32135MChat Q4_K_M·1354.4 t/s	Q4_K_M	0.4 GB3%	1354.4 t/s	2K	S100, Runs great
MiniCPM OCR 1.01.3BVisionFunctions Q4_K_M·515.4 t/s	Q4_K_M	1.5 GB9%	515.4 t/s	262K	S100, Runs great
GPT OSS 20B Heretic20.9BChatFunctions Q4_K_M·173.4 t/s	Q4_K_M	13.1 GB82%	173.4 t/s	131K	S96, Runs great
MIST Mini 8B Thinking8.0BChatReasoning Q4_K_M·108.1 t/s	Q4_K_M	7.3 GB45%	108.1 t/s	131K	S93, Runs great
SmolVLM2 256M Video Instruct256MVision Q4_K_M·1164.8 t/s	Q4_K_M	0.6 GB4%	1164.8 t/s	8K	S100, Runs great
Sweep Next Edit v2 7B7.6BChatCode Q4_K_M·116.7 t/s	Q4_K_M	5.8 GB36%	116.7 t/s	33K	S94, Runs great
Supra Mini V5 8M8MChat Q4_K_M·1820.0 t/s	Q4_K_M	0.3 GB2%	1820.0 t/s	1K	S100, Runs great
GPT X2 125M144MChat BF16·910.0 t/s	BF16	0.6 GB4%	910.0 t/s	1K	S100, Runs great

RTX 5080 Laptop GPU specs, power and PSU

Specs

Architecture: Blackwell
VRAM: 16 GB GDDR7
Memory bandwidth: 896.0 GB/s
FP16 (dense): 70.26 TFLOPS
CUDA cores: 7,680
Tensor cores: 240
Compute capability: 12.0
TDP: 150 W
Released: 2025-03-01

RTX 5080 Laptop GPU FAQ

What is the largest model the RTX 5080 Laptop GPU can run?

Gemma 4 26B A4B IT (25.8B) is the largest at 4-bit or better: at Q4_K_S it uses 15.5 GB of 16 GB, and runs at about 164 tok/s. At 3-bit, a bigger model fits too: Gemma 2 27B IT (27.2B) at IQ3_M, 15.0 GB.

Can the RTX 5080 Laptop GPU run Qwen3.5 9B?

Yes. Qwen3.5 9B fits at Q4_K_M, using 6.6 GB of 16 GB, and it is the best model that runs well on this card.

Can the RTX 5080 Laptop GPU run 70B models?

No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 16 GB on this card.

How many tokens per second does the RTX 5080 Laptop GPU give?

About 91.6 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 2.5 s on a 4K-token prompt.

What power supply does the RTX 5080 Laptop GPU need?

Plan a 300 W PSU (2× the 150 W TDP).

Is 16 GB enough for coding and agents?

Yes for coding: GPT OSS 20B, the best of the 2 coding models that fit, runs at about 173 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.

Is renting cheaper than buying an RTX 5080 Laptop GPU?

An RTX 3090 (24 GB) rents from $0.18/hr. With regular daily use, owning pays off.

Best coding LLMs 16 GB VRAM 24 GB VRAM GPUs with 16 GB or more RTX 5090 RTX 4090