Best AI Models for NVIDIA GeForce RTX 2080 SUPER Laptop GPU (8 GB)

Runs 7–9B models well; 14B doesn't fit in 8 GB.

8 GB GDDR6448 GB/s38 TFLOPS FP16150 WTuring 2020

8 GB with Tensor Cores and flash-attention (cc 7.5), 448 GB/s: ~7-8B Q4 models; the 8 GB, not compute, is the limit. Max-Q versions are noticeably slower.

What LLMs can the RTX 2080 SUPER Laptop GPU run?

#	Model	Speed	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 45.8 t/s	45.8 tok/s	Q4_K_M · 6.6 GB
2	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s	89.3 tok/s	Q4_K_M · 3.4 GB
3	Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 47.7 t/s	47.7 tok/s	Q4_K_M · 7.3 GB
4	Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 103 t/s	103 tok/s	Q4_K_M · 5.0 GB
5	Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 221 t/s	221 tok/s	Q4_K_M · 5.5 GB

#	Model	Speed	Reasoning Score	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 45.8 t/s	45.8 tok/s		Q4_K_M · 6.6 GB
2	Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 103 t/s	103 tok/s		Q4_K_M · 5.0 GB
3	Qwen2.5 7B InstructAlibaba Q4_K_M · 5.8 GB · 58.4 t/s	58.4 tok/s		Q4_K_M · 5.8 GB
4	Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 47.7 t/s	47.7 tok/s	overall	Q4_K_M · 7.3 GB
5	Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 221 t/s	221 tok/s		Q4_K_M · 5.5 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 89.3 t/s	89.3 tok/s	Q4_K_M · 3.4 GB
2	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 45.8 t/s	45.8 tok/s	Q4_K_M · 6.6 GB
3	Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 54.7 t/s	54.7 tok/s	Q4_K_M · 5.6 GB
4	Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 221 t/s	221 tok/s	Q4_K_M · 5.5 GB
5	Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 98.4 t/s	98.4 tok/s	Q4_K_M · 4.3 GB

#	Model	Speed ↓	Quant · VRAM
1	Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 89.3 t/s	89.3 tok/s	Q4_K_M · 3.4 GB
2	Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 221 t/s	221 tok/s	Q4_K_M · 5.5 GB
3	Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 54.7 t/s	54.7 tok/s	Q4_K_M · 5.6 GB
4	MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M · 6.5 GB · 46.9 t/s	46.9 tok/s	Q4_K_M · 6.5 GB
5	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 45.8 t/s	45.8 tok/s	Q4_K_M · 6.6 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 45.8 t/s	45.8 tok/s	Q4_K_M · 6.6 GB
2	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s	89.3 tok/s	Q4_K_M · 3.4 GB
3	Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 103 t/s	103 tok/s	Q4_K_M · 5.0 GB
4	MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M · 6.5 GB · 46.9 t/s	46.9 tok/s	Q4_K_M · 6.5 GB
5	LFM3 Omni 2BLiquid AI Q4_K_M · 2.4 GB · 171 t/s	171 tok/s	Q4_K_M · 2.4 GB

See all 218 models on the RTX 2080 SUPER Laptop GPU →Best quant that fits · speeds estimated on this card

About the RTX 2080 SUPER Laptop GPU

The RTX 2080 SUPER Laptop GPU runs 7–9B models well. Turing generation, released 2020.

GenerationTuringreleased 2020

RTX 2080 SUPER Laptop GPU strengths and weaknesses

Strengths

Runs 7–9B models: Qwen3.5 9B at 45.8 tok/s in 6.6 GB
Low power draw: 150 W board power; a 300 W PSU is enough

Weaknesses

14B models don't fit: Qwen3 14B needs 11.9 GB at Q4 vs 8 GB here
Little room for context: Qwen3.5 9B uses 6.6 of 8 GB, 1.4 GB left
No FP8 or NVFP4: Turing predates both; use GGUF, AWQ or GPTQ builds

Similar GPUs

Cards people weigh against the RTX 2080 SUPER Laptop GPU, each with the one reason it is worth a look.

About as fast, same 8 GBRTX 3070 TiNVIDIA · 8 GB · Ampere

same 8 GB$599rent $0.18/hr62.2 tok/s

8 GB more for bigger modelsRTX 4080NVIDIA · 16 GB · Ada Lovelace

▲ 8 GB$1,199rent $0.25/hr73.3 tok/s

8 GB more for bigger modelsRTX 4080 SUPERNVIDIA · 16 GB · Ada Lovelace

▲ 8 GB$999rent $0.32/hr75.2 tok/s

2 GB more for bigger modelsRTX 3080NVIDIA · 10 GB · Ampere

▲ 2 GB$699rent $0.11/hr77.7 tok/s

8 GB more for bigger modelsRTX 5080 Laptop GPUNVIDIA · 16 GB · Blackwell

▲ 8 GB91.6 tok/s

Best value near the RTX 2080 SUPER Laptop GPURTX 5070 TiNVIDIA · 16 GB · Blackwell

▲ 8 GB$1,170rent $0.17/hr91.6 tok/s

RTX 2080 SUPER Laptop GPU tokens per second by model size

Output speed depends on memory bandwidth (448 GB/s). Grey rows need more than 8 GB.

9BQwen3.5 9B · Q4_K_M · 6.6 GB
45.8tok/s
14BQwen3 14B · not at 4-bit
needs 11.9 GB
27–31BQwen3.8 27B · not at 4-bit
needs 17.9 GB
35B MoEQwen3.6 35B A3B · not at 4-bit
needs 22.0 GB
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 218 models on the RTX 2080 SUPER 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 2080 SUPER Laptop GPU — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Qwen3 VL 4B Thinking4.4BVisionFunctions Q4_K_M·92.4 t/s	Q4_K_M	4.5 GB56%	92.4 t/s	262K	S90, Runs great
Granite 4.0 Micro3.4BChatFunctions Q4_K_M·116.0 t/s	Q4_K_M	3.7 GB46%	116.0 t/s	131K	S94, Runs great
InternVL3 8B7.9BVision Q4_K_M·55.6 t/s	Q4_K_M	7.8 GB98%	55.6 t/s	—	C44, Usable
Nanonets OCR S3.8BVision Q4_K_M·110.7 t/s	Q4_K_M	3.2 GB40%	110.7 t/s	128K	S93, Runs great
LFM2 VL 1.6B1.6BVision Q4_K_M·220.6 t/s	Q4_K_M	1.5 GB19%	220.6 t/s	128K	S100, Runs great
Gemma 3 1B IT1000MChat Q4_K_M·441.2 t/s	Q4_K_M	2.1 GB26%	441.2 t/s	33K	S100, Runs great
Idefics3 8B Llama38.5BVision Q4_K_M·51.5 t/s	Q4_K_M	7.5 GB94%	51.5 t/s	131K	B59, Decent
Gemma 3n E2B IT5.4BVision Q4_K_M·81.1 t/s	Q4_K_M	5.9 GB74%	81.1 t/s	—	S88, Runs great
DeepSeek R1 Distill Qwen 7B7.6BChatReasoningFunctions Q4_K_M·58.4 t/s	Q4_K_M	5.8 GB73%	58.4 t/s	131K	A83, Runs well
Gemma 3 4B Pt4.3BVision Q4_K_M·102.5 t/s	Q4_K_M	5 GB63%	102.5 t/s	—	S92, Runs great
Phi 4 Mini Instruct3.8BChatCodeFunctions Q4_K_M·101.5 t/s	Q4_K_M	4.8 GB59%	101.5 t/s	131K	S92, Runs great
Mistral 7B Instruct v0.37.2BChatFunctions Q4_K_M·59.2 t/s	Q4_K_M	6.8 GB85%	59.2 t/s	33K	A76, Runs well
Gemma 4 E2B5.1BChat Q4_K_M·84.9 t/s	Q4_K_M	3.5 GB44%	84.9 t/s	131K	S88, Runs great
Gemma 2 9B IT9.2BChat Q4_K_M·47.7 t/s	Q4_K_M	7.3 GB91%	47.7 t/s	8K	B63, Decent
Phi 4 Mini Reasoning3.8BChatMathCodeReasoningFunctions Q4_K_M·101.5 t/s	Q4_K_M	4.8 GB59%	101.5 t/s	131K	S92, Runs great

RTX 2080 SUPER Laptop GPU specs, power and PSU

Specs

Architecture: Turing
VRAM: 8 GB GDDR6
Memory bandwidth: 448.0 GB/s
FP16 (dense): 38.34 TFLOPS
CUDA cores: 3,072
Tensor cores: 384
Compute capability: 7.5
TDP: 150 W
Released: 2020-04-02

RTX 2080 SUPER Laptop GPU FAQ

What is the largest model the RTX 2080 SUPER Laptop GPU can run?

Qwen3.5 9B (9.7B) is the largest at 4-bit or better: at Q4_K_M it uses 6.6 GB of 8 GB, and runs at about 45.8 tok/s.

Can the RTX 2080 SUPER Laptop GPU run 14B models?

No. Qwen3 14B needs 11.9 GB at Q4, more than the 8 GB on this card.

How many tokens per second does the RTX 2080 SUPER Laptop GPU give?

About 45.8 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 4.6 s on a 4K-token prompt.

What power supply does the RTX 2080 SUPER Laptop GPU need?

Plan a 300 W PSU (2× the 150 W TDP).

Is renting cheaper than buying an RTX 2080 SUPER Laptop GPU?

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

Best coding LLMs 8 GB VRAM 12 GB VRAM GPUs with 8 GB or more RTX 5080 RTX 3080 Ti