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 $0.14/hr95.7 tok/s

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

same 16 GB$2,394rent $0.26/hr98.1 tok/s

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

▲ 8 GB$1,999rent $0.26/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
Cosmos Reason2 2B2.4BVisionChatReasoningFunctions Q4_K_M·361.7 t/s	Q4_K_M	3.5 GB22%	361.7 t/s	—	S100, Runs great
Raon 1 Nano3.2BChatFunctions Q4_K_M·276.0 t/s	Q4_K_M	4.1 GB26%	276.0 t/s	262K	S100, Runs great
Intern Decision 4B4.5BVisionFunctions Q4_K_M·182.6 t/s	Q4_K_M	3.4 GB21%	182.6 t/s	262K	S99, Runs great
LocateAnything 3B3.8BVisionFunctions Q4_K_M·218.1 t/s	Q4_K_M	3.2 GB20%	218.1 t/s	33K	S100, Runs great
ALoDLM 8B8.2BChatCodeFunctions Q4_K_M·105.5 t/s	Q4_K_M	7.6 GB48%	105.5 t/s	41K	S92, Runs great
Ornith 1.5 9B OBLITERATED9.7BChatFunctions Q4_K_M·91.6 t/s	Q4_K_M	6.6 GB41%	91.6 t/s	262K	S90, Runs great
Phi 414.7BChatMathCode Q4_K_M·61.2 t/s	Q4_K_M	12.5 GB78%	61.2 t/s	16K	A84, Runs well
Intern Decision 0.8B853MVisionFunctions Q4_K_M·677.2 t/s	Q4_K_M	0.9 GB6%	677.2 t/s	262K	S100, Runs great
MinerU2.5 Pro 2604 1.2B1.2BVision Q4_K_M·571.0 t/s	Q4_K_M	1.1 GB7%	571.0 t/s	8K	S100, Runs great
LensVLM 9B9.4BVisionFunctions Q4_K_M·93.8 t/s	Q4_K_M	6.5 GB41%	93.8 t/s	262K	S90, Runs great
Qwen3.8 27B DFlash21.9BChat Q4_K_M·388.3 t/s	Q4_K_M	1.9 GB12%	388.3 t/s	262K	S100, Runs great
Intern Decision 2B2.2BVisionFunctions Q4_K_M·336.6 t/s	Q4_K_M	1.8 GB11%	336.6 t/s	262K	S100, Runs great
MiniCPM5 2B DSpark324MChat Q4_K_M·1164.8 t/s	Q4_K_M	0.6 GB4%	1164.8 t/s	131K	S100, Runs great
TwIL LM33.1BChatReasoningFunctions Q4_K_M·253.2 t/s	Q4_K_M	3.4 GB21%	253.2 t/s	66K	S100, Runs great
NVIDIA Nemotron 3.5 Bolt E4B 4GB4BChat Q4_K_M·220.6 t/s	Q4_K_M	4.8 GB30%	220.6 t/s	—	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 $0.14/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