NVIDIA · Turing · 8 GB VRAM tier
Best AI Models for NVIDIA GeForce RTX 2080 Laptop GPU (8 GB)
Runs 7–9B models well; 14B doesn't fit in 8 GB.
8 GB GDDR6448 GB/s37 TFLOPS FP16150 WTuring 2019
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. Power limit varies a lot by chassis.
| # | 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 2080 Laptop GPU →Best quant that fits · speeds estimated on this card
The RTX 2080 Laptop GPU runs 7–9B models well. Turing generation, released 2019.
GenerationTuringreleased 2019
RTX 2080 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
Cards people weigh against the RTX 2080 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 $0.16/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 $0.26/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 2080 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 2080 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 |
|---|---|---|---|---|---|---|
Tiny Starcoder PyChatCode | Q4_K_M | 0.5 GB | 2647.3 t/s | 8K | S100, Runs great | |
Qwen2.5 Coder 0.5B InstructChatCodeFunctions | Q4_K_M | 0.8 GB | 469.7 t/s | 33K | S100, Runs great | |
Gemma 3 270MChat | Q4_K_M | 1.2 GB | 1617.8 t/s | — | S100, Runs great | |
Medgemma 4B ITVision | Q4_K_M | 5 GB | 102.5 t/s | — | S92, Runs great | |
Kumru 2B BaseChat | Q4_K_M | 2.2 GB | 158.3 t/s | 8K | S97, Runs great | |
Qwen3 0.6B BaseChat | Q4_K_M | 1.6 GB | 373.3 t/s | 33K | S100, Runs great | |
Mellum2 12B A2.5B ThinkingChatFunctions | Q4_K_M | 7.8 GB | 146.6 t/s | 131K | B62, Decent | |
Mistral 7B v0.3Chat | Q4_K_M | 6.8 GB | 59.2 t/s | 33K | A76, Runs well | |
DeepSeek R1 Distill Llama 8BChatReasoningFunctions | Q4_K_M | 7.3 GB | 54.0 t/s | 131K | A65, Runs well | |
| Q4_K_M | 0.3 GB | 29120.0 t/s | — | S100, Runs great | ||
Qwen3 VL 8B ThinkingVisionFunctions | Q4_K_M | 8.0 GB | 49.7 t/s | 262K | C44, Usable | |
Qwen3 4B BaseChat | Q4_K_M | 4.2 GB | 100.4 t/s | 33K | S91, Runs great | |
MiMo 7B BaseChat | Q4_K_M | 7.4 GB | 54.9 t/s | 33K | B62, Decent | |
| Q4_K_M | 0.4 GB | 970.7 t/s | 8K | S100, Runs great | ||
Nemotron Cascade 8BChatReasoning | Q4_K_M | 7.5 GB | 53.9 t/s | 33K | B60, Decent |
Specs
- Architecture
- Turing
- VRAM
- 8 GB GDDR6
- Memory bandwidth
- 448.0 GB/s
- FP16 (dense)
- 37.45 TFLOPS
- CUDA cores
- 2,944
- Tensor cores
- 368
- Compute capability
- 7.5
- TDP
- 150 W
- Released
- 2019-01-29
RTX 2080 Laptop GPU FAQ
What is the largest model the RTX 2080 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 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 Laptop GPU give?
About 45.8 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 4.7 s on a 4K-token prompt.
What power supply does the RTX 2080 Laptop GPU need?
Plan a 300 W PSU (2× the 150 W TDP).
Is renting cheaper than buying an RTX 2080 Laptop GPU?
An RTX 3090 (24 GB) rents from $0.18/hr. With regular daily use, owning pays off.