NVIDIA · Turing
Best AI Models for NVIDIA GeForce RTX 2060 Laptop GPU (6 GB)
Runs only small models well; 7–9B only just fits.
6 GB GDDR6336 GB/s18 TFLOPS FP1690 WTuring 2019
Laptop RTX 2060 has 6 GB and Tensor Cores with flash-attention (cc 7.5): holds ~7B Q4 models with a modest context. Clocks and speed depend heavily on the chassis power limit.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 76.9 t/s | 76.9 tok/s | Q4_K_M · 5.0 GB | |
| 3 | Gemma 4 E2B ITGoogle Q4_K_M · 3.5 GB · 63.7 t/s | 63.7 tok/s | Q4_K_M · 3.5 GB | |
| 4 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 73.8 t/s | 73.8 tok/s | Q4_K_M · 4.3 GB | |
| 5 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 115 t/s | 115 tok/s | Q4_K_M · 2.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Q4_K_M · 5.0 GB · 76.9 t/s | 76.9 tok/s | Q4_K_M · 5.0 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | overall | Q4_K_M · 3.4 GB |
| 3 | Gemma 4 E2B ITGoogle Q4_K_M · 3.5 GB · 63.7 t/s | 63.7 tok/s | Q4_K_M · 3.5 GB | |
| 4 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 73.8 t/s | 73.8 tok/s | Q4_K_M · 4.3 GB | |
| 5 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 115 t/s | 115 tok/s | Q4_K_M · 2.5 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Gemma 4 E2B ITGoogle Q4_K_M · 3.5 GB · 63.7 t/s | 63.7 tok/s | Q4_K_M · 3.5 GB | |
| 3 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 73.8 t/s | 73.8 tok/s | Q4_K_M · 4.3 GB | |
| 4 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 115 t/s | 115 tok/s | Q4_K_M · 2.5 GB | |
| 5 | LFM3 1.7BLiquid AI Q4_K_M · 2.1 GB · 152 t/s | 152 tok/s | Q4_K_M · 2.1 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 179 t/s | 179 tok/s | Q4_K_M · 5.5 GB | |
| 3 | MiniCPM5 2BOpenBMB Q4_K_M · 2.5 GB · 115 t/s | 115 tok/s | Q4_K_M · 2.5 GB | |
| 4 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 73.8 t/s | 73.8 tok/s | Q4_K_M · 4.3 GB | |
| 5 | Gemma 4 E2B ITGoogle Q4_K_M · 3.5 GB · 63.7 t/s | 63.7 tok/s | Q4_K_M · 3.5 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 67.0 t/s | 67.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 76.9 t/s | 76.9 tok/s | Q4_K_M · 5.0 GB | |
| 3 | LFM3 Omni 2BLiquid AI Q4_K_M · 2.4 GB · 129 t/s | 129 tok/s | Q4_K_M · 2.4 GB | |
| 4 | MiniCPM V 4.7 1BOpenBMB Q4_K_M · 1.6 GB · 171 t/s | 171 tok/s | Q4_K_M · 1.6 GB | |
| 5 | Cosmos Reason2 2BNVIDIA Q4_K_M · 3.5 GB · 136 t/s | 136 tok/s | Q4_K_M · 3.5 GB |
See all 193 models on the RTX 2060 Laptop GPU →Best quant that fits · speeds estimated on this card
The RTX 2060 Laptop GPU runs only small models well. Turing generation, released 2019.
GenerationTuringreleased 2019
RTX 2060 Laptop GPU strengths and weaknesses
Strengths
- Runs 7–9B models: Qwen3.5 9B at 39.6 tok/s in 5.8 GB
- Low power draw: 90 W board power; a 200 W PSU is enough
Weaknesses
- 14B models don't fit: Qwen3 14B needs 11.9 GB at Q4 vs 6 GB here
- Little room for context: Qwen3.5 9B uses 5.8 of 6 GB, 0.2 GB left
- No FP8 or NVFP4: Turing predates both; use GGUF, AWQ or GPTQ builds
Cards people weigh against the RTX 2060 Laptop GPU, each with the one reason it is worth a look.
2 GB more for bigger modelsRTX 4060NVIDIA · 8 GB · Ada Lovelace
▲ 2 GBabout $520rent $0.08/hr27.8 tok/s
2 GB more for bigger modelsA750Intel · 8 GB · Alchemist▲ 2 GBabout $28940.3 tok/s
Best value near the RTX 2060 Laptop GPURTX 5060NVIDIA · 8 GB · Blackwell▲ 2 GBabout $46045.8 tok/s
2 GB more for bigger modelsRTX 3070 TiNVIDIA · 8 GB · Ampere▲ 2 GBabout $599rent $0.18/hr62.2 tok/s
5 GB more for bigger modelsRTX 2080 TiNVIDIA · 11 GB · Turing▲ 5 GBabout $999rent $0.10/hr63.0 tok/s
6 GB more for bigger modelsRTX 5070 Ti Laptop GPUNVIDIA · 12 GB · Blackwell▲ 6 GB68.7 tok/s
Output speed depends on memory bandwidth (336 GB/s). Grey rows need more than 6 GB.
- 39.6tok/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 193 models on the RTX 2060 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 |
|---|---|---|---|---|---|---|
Chandra Ocr 2VisionFunctions | Q4_K_M | 3.8 GB | 59.8 t/s | 262K | A84, Runs well | |
LFM2.5 230MChatFunctions | Q4_K_M | 0.6 GB | 436.8 t/s | 128K | S100, Runs great | |
NVIDIA Nemotron Labs Dory1.0 4B BF16ChatFunctions | Q4_K_M | 5.7 GB | 71.4 t/s | 262K | B62, Decent | |
InternVL3 1BVision | Q4_K_M | 2.0 GB | 352.3 t/s | — | S100, Runs great | |
MiniCPM V 4Vision | Q4_K_M | 3.1 GB | 78.6 t/s | 33K | S87, Runs great | |
SmolVLM 500M InstructVision | Q4_K_M | 0.9 GB | 316.5 t/s | 8K | S100, Runs great | |
Moondream2Vision | Q4_K_M | 3.0 GB | 172.0 t/s | — | S98, Runs great | |
Ornith 1.5 9BChatFunctions | Q3_K_L | 5.8 GB | 39.6 t/s | 262K | B48, Decent | |
Llama 3.2 3B InstructChatFunctions | Q4_K_M | 4.1 GB | 103.0 t/s | 131K | S92, Runs great | |
SmolVLM 256M InstructVision | Q4_K_M | 0.6 GB | 436.8 t/s | 8K | S100, Runs great | |
SmolVLM2 2.2B InstructVision | Q4_K_M | 2.3 GB | 147.6 t/s | 8K | S97, Runs great | |
LFM2.5 VL 450MVisionFunctions | Q4_K_M | 0.8 GB | 341.3 t/s | 128K | S100, Runs great | |
Qwen3 VL 4B InstructVisionFunctions | Q4_K_M | 4.5 GB | 69.3 t/s | 262K | S86, Runs great | |
OvisOCR3VisionFunctions | Q4_K_M | 0.9 GB | 254.0 t/s | 262K | S100, Runs great | |
Surya Ocr 2Vision | Q4_K_M | 0.8 GB | 287.4 t/s | 262K | S100, Runs great |
Specs
- Architecture
- Turing
- VRAM
- 6 GB GDDR6
- Memory bandwidth
- 336.0 GB/s
- FP16 (dense)
- 18.43 TFLOPS
- CUDA cores
- 1,920
- Tensor cores
- 240
- Compute capability
- 7.5
- TDP
- 90 W
- Released
- 2019-01-29
RTX 2060 Laptop GPU FAQ
What is the largest model the RTX 2060 Laptop GPU can run?
Qwen2.5 7B Instruct (7.6B) is the largest at 4-bit or better: at Q4_K_M it uses 5.8 GB of 6 GB, and runs at about 43.8 tok/s. At 3-bit, a bigger model fits too: Gemma 2 9B IT (9.2B) at IQ3_M, 5.7 GB.
Can the RTX 2060 Laptop GPU run Qwen3.5 4B?
Yes. Qwen3.5 4B fits at Q4_K_M, using 3.4 GB of 6 GB, and it is the best model that runs well on this card.
Can the RTX 2060 Laptop GPU run 14B models?
No. Qwen3 14B needs 11.9 GB at Q4, more than the 6 GB on this card.
How many tokens per second does the RTX 2060 Laptop GPU give?
About 67.0 tok/s on Qwen3.5 4B at Q4_K_M, with the first token after 4.6 s on a 4K-token prompt.
What power supply does the RTX 2060 Laptop GPU need?
Plan a 200 W PSU (2× the 90 W TDP).
Is renting cheaper than buying an RTX 2060 Laptop GPU?
An RTX 3080 Ti (12 GB) rents from $0.13/hr. With regular daily use, owning pays off.