Best AI Models for NVIDIA GeForce RTX 2070 SUPER (8 GB)

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

8 GB GDDR6448 GB/s36 TFLOPS FP16215 WTuring 2019

Turing 8 GB: Tensor Cores and flash-attention (cc 7.5), 448 GB/s; 8 GB caps you at ~7-8B models at Q4.

What LLMs can the RTX 2070 SUPER 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 2070 SUPER →Best quant that fits · speeds estimated on this card

About the RTX 2070 SUPER

The RTX 2070 SUPER is a solid mid-range card in the 8 GB class: it runs 7–9B models well. It is more efficient per watt than most 8 GB cards, and the best speed per dollar of the cards compared at ≈$206 used. Turing generation, released 2019.

Speed in 8 GB#3448 GB/s

Per watt#100.21 tok/s per W on Qwen3.5 9B (9B params)

Per dollar#122.2 tok/s per $100 on Qwen3.5 9B (9B params)

GenerationTuringreleased 2019

RTX 2070 SUPER strengths and weaknesses

Strengths

Runs 7–9B models: Qwen3.5 9B at 45.8 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 2070 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 2070 SUPER8 GB	about $499	45.8	9.2	21.3
RTX 507012 GB	about $840	68.7	8.2	27.5
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 2070 SUPER8 GB	about $206	45.8	22.2	21.3
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
RTX 507012 GB	about $736	68.7	9.3	27.5
RTX 407012 GB	about $575	51.5	9.0	25.8
RTX 4070 SUPER12 GB	about $675	51.5	7.6	23.4
RTX 4060 Ti 8GB8 GB	about $400	29.4	7.4	18.4
RTX 4070 Ti12 GB	about $722	51.5	7.1	18.1

Similar GPUs

Cards people weigh against the RTX 2070 SUPER, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.

4 GB more and 12% fasterRTX 4070NVIDIA · 12 GB · Ada Lovelace

▲ 12% speed▲ 4 GB$599rent $0.13/hr

4 GB more and 50% fasterRTX 5070NVIDIA · 12 GB · Blackwell

▲ 50% speed▲ 4 GB$840rent $0.14/hr

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

same speedsame 8 GB$460

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

▼ 20% speed▲ 4 GB$329rent $0.06/hr

12% slower, same 8 GBA580Intel · 8 GB · Alchemist

▼ 12% speedsame 8 GB$179

8 GB more and 60% fasterRTX 4080NVIDIA · 16 GB · Ada Lovelace

▲ 60% speed▲ 8 GB$1,199rent $0.25/hr

RTX 2070 SUPER 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 842 models on the RTX 2070 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 2070 SUPER — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Gemma 1.1 2B IT2.5BChat Q4_K_M·176.5 t/s	Q4_K_M	3.5 GB44%	176.5 t/s	—	S99, Runs great
Yi 6B Chat6.1BChat Q4_K_M·71.5 t/s	Q4_K_M	4.2 GB53%	71.5 t/s	4K	S86, Runs great
RedPajama INCITE 7B Base7BChat Q4_K_M·63.0 t/s	Q4_K_M	4.6 GB58%	63.0 t/s	2K	A84, Runs well
Vllm Translategemma 12B IT13.2BChatTranslation Q2_K·43.7 t/s	Q2_K	7.5 GB93%	43.7 t/s	131K	B57, Decent
Text2cypher Gemma 2 9B IT Finetuned 2024v19BChat Q3_K_M·60.3 t/s	Q3_K_M	7.5 GB94%	60.3 t/s	—	B62, Decent
II Medical 8B8.2BChat Q4_K_M·52.8 t/s	Q4_K_M	7.6 GB95%	52.8 t/s	41K	B58, Decent
Codegemma 2B2.5BChatCode Q4_K_M·176.5 t/s	Q4_K_M	3.5 GB44%	176.5 t/s	—	S99, Runs great
SmolLM 360M Instruct362MChat Q4_K_M·485.3 t/s	Q4_K_M	0.6 GB8%	485.3 t/s	2K	S100, Runs great
SmolLM2 360M362MChat Q4_K_M·485.3 t/s	Q4_K_M	0.8 GB11%	485.3 t/s	8K	S100, Runs great
Wildguard7.2BChat Q4_K_M·60.9 t/s	Q4_K_M	7.3 GB91%	60.9 t/s	—	A68, Runs well
SmolLM 360M362MChat Q4_K_M·485.3 t/s	Q4_K_M	0.6 GB8%	485.3 t/s	2K	S100, Runs great
BioMistral 7B7BChat Q4_K_M·61.0 t/s	Q4_K_M	6.7 GB83%	61.0 t/s	33K	A80, Runs well
Qwen 1 8B1.8BChat Q4_K_M·240.7 t/s	Q4_K_M	1.9 GB24%	240.7 t/s	8K	S100, Runs great
Qwen3 14B14.8BChatFunctions IQ2_M·51.8 t/s	IQ2_M	8.0 GB100%	51.8 t/s	41K	C44, Usable
Qwen3 8B Base8.2BChat Q4_K_M·52.8 t/s	Q4_K_M	7.6 GB95%	52.8 t/s	33K	B58, Decent

RTX 2070 SUPER specs, power and PSU

Specs

Architecture: Turing
VRAM: 8 GB GDDR6
Memory bandwidth: 448.0 GB/s
FP16 (dense): 36.25 TFLOPS
CUDA cores: 2,560
Tensor cores: 320
Compute capability: 7.5
TDP: 215 W
Released: 2019-07-09
MSRP: $499

RTX 2070 SUPER FAQ

Is the RTX 2070 SUPER still worth it in 2026?

At ≈$206 used you get 22.2 tok/s per $100 on Qwen3.5 9B, which ranks 1st of the 8 cards compared here; it is also the 3rd fastest out of 19 cards with 8 GB. It runs 7–9B models well; 14B doesn't fit in 8 GB.

What is the largest model the RTX 2070 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 45.8 tok/s.

Can the RTX 2070 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 2070 SUPER give?

About 45.8 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 4.8 s on a 4K-token prompt.

What power supply does the RTX 2070 SUPER need?

Plan a 450 W PSU (2× the 215 W TDP).

Is renting cheaper than buying an RTX 2070 SUPER?

An RTX 3090 (24 GB) rents from $0.18/hr. Renting costs as much as buying after ≈1,100 hours (≈0.8 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