NVIDIA · Turing · 8 GB VRAM tier
Best AI Models for NVIDIA GeForce RTX 2070 SUPER Laptop GPU (8 GB)
Runs 7–9B models well; 14B doesn't fit in 8 GB.
8 GB GDDR6448 GB/s28 TFLOPS FP16115 WTuring 2020
8 GB with Tensor Cores and flash-attention (cc 7.5), 448 GB/s: fits ~7-8B Q4 models with room for context. Max-Q versions run at lower clocks and bandwidth.
| # | Model | Speed | llmrun 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 | 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 | Long context Score | 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 ↓ | llmrun Score | 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 | 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 | Vision 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 | 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 | 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 Laptop GPU →Best quant that fits · speeds estimated on this card
The RTX 2070 SUPER Laptop GPU runs 7–9B models well. Turing generation, released 2020.
GenerationTuringreleased 2020
RTX 2070 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: 115 W board power; a 250 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
- Slow prompt reading: A 4K-token prompt on Qwen3.5 9B waits 6.2 s for the first token
- No FP8 or NVFP4: Turing predates both; use GGUF, AWQ or GPTQ builds
Cards people weigh against the RTX 2070 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 GBabout $599rent $0.15/hr62.2 tok/s
8 GB more for bigger modelsRTX 4080NVIDIA · 16 GB · Ada Lovelace▲ 8 GBabout $1,199rent $0.25/hr73.3 tok/s
8 GB more for bigger modelsRTX 4080 SUPERNVIDIA · 16 GB · Ada Lovelace▲ 8 GBabout $999rent $0.23/hr75.2 tok/s
2 GB more for bigger modelsRTX 3080NVIDIA · 10 GB · Ampere▲ 2 GBabout $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 2070 SUPER Laptop GPURTX 5070 TiNVIDIA · 16 GB · Blackwell▲ 8 GBabout $1,170rent $0.20/hr91.6 tok/s
Output speed depends on memory bandwidth (448 GB/s). Grey rows need more than 8 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB45.8tok/s
- 14BQwen3 14B · not at 4-bitneeds 11.9 GB
- 27–31BQwen3.8 27B · not at 4-bitneeds 17.9 GB
- 35B MoEQwen3.6 35B A3B · not at 4-bitneeds 22.0 GB
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 842 models on the RTX 2070 SUPER Laptop GPU
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
Luciole 8B BaseChat | Q4_K_M | 5.6 GB | 52.2 t/s | 131K | A81, Runs well | |
Granite 4.0 Tiny PreviewChatFunctions | Q4_K_M | 5.6 GB | 185.5 t/s | 131K | S99, Runs great | |
| Q4_K_M | 7.3 GB | 54.0 t/s | 131K | A65, Runs well | ||
Phi 2ChatCode | Q4_K_M | 2.6 GB | 110.3 t/s | 2K | S93, Runs great | |
Vicuna 7B V1.5Chat | Q4_K_M | 6.6 GB | 52.3 t/s | 4K | A77, Runs well | |
| Q4_K_M | 6.9 GB | 65.4 t/s | — | A77, Runs well | ||
| Q4_K_M | 1.0 GB | 288.3 t/s | 2K | S100, Runs great | ||
Gemma 3n E4B ITVision | Q4_K_M | 7.7 GB | 56.2 t/s | — | B56, Decent | |
Granite 3.1 2B InstructChatFunctions | Q4_K_M | 3.2 GB | 146.3 t/s | 131K | S97, Runs great | |
Mistral 7B v0.2Chat | Q4_K_M | 6.8 GB | 59.3 t/s | 33K | A77, Runs well | |
Polyglot Ko 1.3BChat | Q4_K_M | 0.9 GB | 306.5 t/s | 2K | S100, Runs great | |
Zephyr 7B BetaChat | Q4_K_M | 6.8 GB | 59.3 t/s | 33K | A77, Runs well | |
Granite Guardian 4.1 8BChatFunctions | Q4_K_S | 7.7 GB | 54.4 t/s | 131K | B57, Decent | |
| BF16 | 7.6 GB | 48.3 t/s | 29K | B56, Decent | ||
LFM2 1.2BChatFunctions | Q4_K_M | 1.2 GB | 272.1 t/s | 128K | S100, Runs great |
Specs
- Architecture
- Turing
- VRAM
- 8 GB GDDR6
- Memory bandwidth
- 448.0 GB/s
- FP16 (dense)
- 28.26 TFLOPS
- CUDA cores
- 2,560
- Tensor cores
- 320
- Compute capability
- 7.5
- TDP
- 115 W
- Released
- 2020-04-02
RTX 2070 SUPER Laptop GPU FAQ
What is the largest model the RTX 2070 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 2070 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 2070 SUPER Laptop GPU give?
About 45.8 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 6.2 s on a 4K-token prompt.
What power supply does the RTX 2070 SUPER Laptop GPU need?
Plan a 250 W PSU (2× the 115 W TDP).
Is renting cheaper than buying an RTX 2070 SUPER Laptop GPU?
An RTX 5080 (16 GB) rents from $0.23/hr. With regular daily use, owning pays off.