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

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

8 GB GDDR6496 GB/s45 TFLOPS FP16250 WTuring 2019

Turing 8 GB with Tensor Cores and flash-attention, 496 GB/s; 8 GB caps you at ~7-8B models at Q4.

What LLMs can the RTX 2080 SUPER run?

#	Model	Speed	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 50.7 t/s	50.7 tok/s	Q4_K_M · 6.6 GB
2	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 98.9 t/s	98.9 tok/s	Q4_K_M · 3.4 GB
3	Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 52.9 t/s	52.9 tok/s	Q4_K_M · 7.3 GB
4	Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 114 t/s	114 tok/s	Q4_K_M · 5.0 GB
5	Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 237 t/s	237 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 · 50.7 t/s	50.7 tok/s		Q4_K_M · 6.6 GB
2	Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 114 t/s	114 tok/s		Q4_K_M · 5.0 GB
3	Qwen2.5 7B InstructAlibaba Q4_K_M · 5.8 GB · 64.6 t/s	64.6 tok/s		Q4_K_M · 5.8 GB
4	Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 52.9 t/s	52.9 tok/s	overall	Q4_K_M · 7.3 GB
5	Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 237 t/s	237 tok/s		Q4_K_M · 5.5 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 98.9 t/s	98.9 tok/s	Q4_K_M · 3.4 GB
2	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 50.7 t/s	50.7 tok/s	Q4_K_M · 6.6 GB
3	Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 60.6 t/s	60.6 tok/s	Q4_K_M · 5.6 GB
4	Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 237 t/s	237 tok/s	Q4_K_M · 5.5 GB
5	Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 109 t/s	109 tok/s	Q4_K_M · 4.3 GB

#	Model	Speed ↓	Quant · VRAM
1	Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 98.9 t/s	98.9 tok/s	Q4_K_M · 3.4 GB
2	Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 237 t/s	237 tok/s	Q4_K_M · 5.5 GB
3	Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 60.6 t/s	60.6 tok/s	Q4_K_M · 5.6 GB
4	MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M · 6.5 GB · 51.9 t/s	51.9 tok/s	Q4_K_M · 6.5 GB
5	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 50.7 t/s	50.7 tok/s	Q4_K_M · 6.6 GB

#	Model	Speed	Quant · VRAM
1	Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 50.7 t/s	50.7 tok/s	Q4_K_M · 6.6 GB
2	Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 98.9 t/s	98.9 tok/s	Q4_K_M · 3.4 GB
3	Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 114 t/s	114 tok/s	Q4_K_M · 5.0 GB
4	MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M · 6.5 GB · 51.9 t/s	51.9 tok/s	Q4_K_M · 6.5 GB
5	LFM3 Omni 2BLiquid AI Q4_K_M · 2.4 GB · 190 t/s	190 tok/s	Q4_K_M · 2.4 GB

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

About the RTX 2080 SUPER

The RTX 2080 SUPER is one of the fastest 8 GB cards: it runs 7–9B models well. It is power-hungry for its speed, and the best speed per dollar of the cards compared at ≈$240 used. Turing generation, released 2019.

Speed in 8 GB#2496 GB/s

Per watt#120.20 tok/s per W on Qwen3.5 9B (9B params)

Per dollar#121.1 tok/s per $100 on Qwen3.5 9B (9B params)

GenerationTuringreleased 2019

RTX 2080 SUPER strengths and weaknesses

Strengths

Runs 7–9B models: Qwen3.5 9B at 50.7 tok/s in 6.6 GB

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

Speed per dollar and per watt

NewUsed

On Qwen3.5 9B (9B params) at Q4_K_M. New prices are compared with new, used with used. The last two columns are the speed you get per $100 of price and per 100 W of board power; higher is better. The RTX 2080 SUPER has no new price, so the New view shows it at its MSRP.

Card	New price	tok/s	tok/s per $100	tok/s per 100 W
RTX 50608 GBBest new value	about $460	45.8	10.0	31.6
RTX 507012 GB	about $840	68.7	8.2	27.5
RTX 5070 Ti16 GB	about $1,170	91.6	7.8	30.5
RX 907016 GB	about $800	60.4	7.6	27.5
RTX 2080 SUPER8 GB	about $699	50.7	7.3	20.3
RTX 40608 GB	about $520	27.8	5.3	24.2

Card	Used price	tok/s	tok/s per $100	tok/s per 100 W
RTX 2080 SUPER8 GB	about $240	50.7	21.1	20.3
RTX 3060 12GB12 GB	about $329	36.8	11.2	21.6
RTX 50608 GBBest used value	about $430	45.8	10.7	31.6
RX 907016 GB	about $650	60.4	9.3	27.5
RTX 407012 GB	about $575	51.5	9.0	25.8
RTX 5070 Ti16 GB	about $1,100	91.6	8.3	30.5
RTX 4070 SUPER12 GB	about $675	51.5	7.6	23.4
RTX 4070 Ti12 GB	about $722	51.5	7.1	18.1

Similar GPUs

Cards people weigh against the RTX 2080 SUPER, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.

8 GB more and 19% fasterRX 9070AMD · 16 GB · RDNA 4

▲ 19% speed▲ 8 GB$800

8 GB more and 81% fasterRTX 5070 TiNVIDIA · 16 GB · Blackwell

▲ 81% speed▲ 8 GB$1,170rent $0.16/hr

Same 8 GB, new with warrantyRTX 5060NVIDIA · 8 GB · Blackwell

▼ 10% speedsame 8 GB$460

4 GB more, but slowerRTX 3060 12GBNVIDIA · 12 GB · Ampere

▼ 27% speed▲ 4 GB$329rent $0.06/hr

23% faster, same 8 GBRTX 3070 TiNVIDIA · 8 GB · Ampere

▲ 23% speedsame 8 GB$599rent $0.13/hr

8 GB more and 45% fasterRTX 4080NVIDIA · 16 GB · Ada Lovelace

▲ 45% speed▲ 8 GB$1,199rent $0.24/hr

RTX 2080 SUPER tokens per second by model size

Output speed depends on memory bandwidth (496 GB/s). Grey rows need more than 8 GB.

9BQwen3.5 9B · Q4_K_M · 6.6 GB
50.7tok/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 842 models on the RTX 2080 SUPER

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 — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Qwen2.5 7B Instruct7.6BChatFunctions Q4_K_M·64.6 t/s	Q4_K_M	5.8 GB73%	64.6 t/s	33K	A84, Runs well
Tiny NemotronHForCausalLM Nano4MChatFunctions Q4_K_M·32240.0 t/s	Q4_K_M	0.3 GB4%	32240.0 t/s	262K	S100, Runs great
Pythia 160M213MChat Q4_K_M·2302.9 t/s	Q4_K_M	0.1 GB2%	2302.9 t/s	2K	S100, Runs great
Qwen3 8B 64k Context 2X Josiefied Uncensored8.2BChatReasoningFunctions Q4_K_M·58.4 t/s	Q4_K_M	7.6 GB95%	58.4 t/s	66K	B60, Decent
Tiny NemotronHForCausalLM Super4MChatFunctions Q4_K_M·32240.0 t/s	Q4_K_M	0.3 GB4%	32240.0 t/s	262K	S100, Runs great
Tiny NemotronHForCausalLM Ultra4MChatFunctions Q4_K_M·32240.0 t/s	Q4_K_M	0.3 GB4%	32240.0 t/s	262K	S100, Runs great
Nanonets OCR S3.8BVision Q4_K_M·122.6 t/s	Q4_K_M	3.2 GB40%	122.6 t/s	128K	S95, Runs great
Granite 4.0 Micro3.4BChatFunctions Q4_K_M·128.4 t/s	Q4_K_M	3.7 GB46%	128.4 t/s	131K	S95, Runs great
Tiny Random Phi33MChat Q4_K_M·1074.7 t/s	Q4_K_M	0.3 GB4%	1074.7 t/s	4K	S100, Runs great
EXAONE 3.5 7.8B Instruct7.8BChat Q4_K_M·62.5 t/s	Q4_K_M	7.7 GB96%	62.5 t/s	33K	B60, Decent
NVIDIA Nemotron Nano 9B v2 Japanese8.9BChat Q4_K_M·53.4 t/s	Q4_K_M	6.0 GB76%	53.4 t/s	131K	A81, Runs well
Transformer 1.3B 100B1.4BChat BF16·107.5 t/s	BF16	3 GB38%	107.5 t/s	2K	S92, Runs great
Qwen3Guard Gen 0.6B752MChat Q4_K_M·370.6 t/s	Q4_K_M	1.7 GB21%	370.6 t/s	33K	S100, Runs great
InternVL3 8B7.9BVision Q4_K_M·61.5 t/s	Q4_K_M	7.8 GB98%	61.5 t/s	—	C44, Usable
MiMo 7B RL7.8BChatFunctions Q4_K_M·60.8 t/s	Q4_K_M	7.4 GB93%	60.8 t/s	33K	B62, Decent

RTX 2080 SUPER specs, power and PSU

Specs

Architecture: Turing
VRAM: 8 GB GDDR6
Memory bandwidth: 496.0 GB/s
FP16 (dense): 44.61 TFLOPS
CUDA cores: 3,072
Tensor cores: 384
Compute capability: 7.5
TDP: 250 W
Released: 2019-07-23
MSRP: $699

RTX 2080 SUPER FAQ

Is the RTX 2080 SUPER still worth it in 2026?

At ≈$240 used you get 21.1 tok/s per $100 on Qwen3.5 9B, which ranks 1st of the 8 cards compared here; it is also the 2nd fastest out of 18 cards with 8 GB. It runs 7–9B models well; 14B doesn't fit in 8 GB.

What is the largest model the RTX 2080 SUPER 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 50.7 tok/s.

Can the RTX 2080 SUPER 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 give?

About 50.7 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 3.9 s on a 4K-token prompt.

What power supply does the RTX 2080 SUPER need?

Plan a 500 W PSU (2× the 250 W TDP).

Is renting cheaper than buying an RTX 2080 SUPER?

An RTX 3090 (24 GB) rents from $0.18/hr. Renting costs as much as buying after ≈1,300 hours (≈0.9 years at 4 h a day).

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