RTX 20ConsumerTuringPCIe 3CUDA
Operating mode
Choose the operating mode for this hardware
Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
About this GPU for AI
The RTX 2060 Super 8GB expands on the RTX 2060 with a wider memory bus, jumping from 6 GB to 8 GB VRAM and bandwidth from 336 to 448 GB/s. This extra VRAM is the key differentiator for AI — 7B models at Q4 and even Q8 fit comfortably, and some 13B models at Q3 are feasible. The 2nd-gen Tensor Cores support INT8/INT4 acceleration. It's a more capable inference card than the base RTX 2060 6GB for only a small price premium.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
legacy-but-capablebudget-friendlylimited-vramhigh-bandwidth-for-class
Specifications
Compute
FP1614 TFLOPS
INT8144 TOPS
ArchitectureTuring
Memory
VRAM8 GB
Bandwidth448 GB/s
General
FamilyRTX 20
SegmentConsumer
InterconnectPCIe 3
Compute PlatformCUDA
MSRP$399
Key Features
CUDA Compute Capability 7.5 (Turing)2nd Gen Tensor Cores (FP16, INT8, INT4)448 GB/s memory bandwidth (GDDR6)14 TFLOPS FP16 computePCIe Gen 3 x168 GB GDDR6 VRAM
For AI Workloads
Strengths
- 8 GB VRAM opens up 7B models at FP16 and allows Q4 inference with headroom for KV cache
- 448 GB/s bandwidth is a meaningful upgrade over the base RTX 2060
- 2nd-gen Tensor Cores accelerate INT8/INT4 inference via llama.cpp
- Good budget option on the used market for basic AI workloads
Considerations
- No FP8 or BF16 support — less efficient than Ampere/Ada for modern inference
- 13B models require aggressive quantization and may still be slow
- PCIe Gen 3 is dated
- 2nd-gen Tensor Cores less efficient per core than Ampere 3rd-gen
Turing is NVIDIA's first-generation RTX architecture, introducing dedicated RT and Tensor Cores to consumer GPUs for the first time. Built on TSMC's 12nm FinFET process.
AI Relevance
The first consumer architecture with Tensor Cores, enabling meaningful acceleration for INT8 and FP16 inference. However, limited VRAM (typically 6-11 GB) restricts modern LLM model sizes.
Process: TSMC 12nmPlatform: CUDATensor Cores: Gen 2Precisions: FP32, FP16, INT8, INT4
Recommendations by Workload
Qwen 3.5 4B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 56.0 tok/s · 22K ctx · llama.cppEST.
Codestral Mamba 7B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 70.0 tok/s · 67K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
30.5BTier 100Needs ~21.0 GB
397BTier 100Needs ~245.3 GB
123BTier 100Needs ~79.4 GB
1000BTier 100Needs ~615.4 GB
1000BTier 100Needs ~615.4 GB
Image & Video Generation
Diffusion Model Compatibility
21 of 52 models can generate images or video on your RTX 2060 Super 8GB
Upgrade paths
Upgrade from RTX 2060 Super 8GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
8
GB
RTX 2060 Super 8GBCategory AvgRTX 3080 10GB
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 | ~~1m 22s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~2m 18s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~2m 49s per image |
| Video Short (25f) | Won't fit | LTX Video 2B | ~~26.7s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~1m 19s/frame |
Gemma 4 E2B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 68.8 tok/s · 96K ctx · llama.cppEST.
Codestral Mamba 7B is viable for Reasoning, but is not the most specialized choice. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 70.0 tok/s · 67K ctx · llama.cppEST.
Granite 4.1 3B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 42.0 tok/s · 59K ctx · llama.cppEST.
Image
| MAGI-1Video | 256×256 | ~1m 12s/frame | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Buying advice
Should you buy RTX 2060 Super 8GB for local AI?
Usable for local AI with limits
Can run 7 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
What will limit you first
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best upgrade itinerary
Unlocks 33 additional models that do not fit on the current setup.
Want more headroom? RTX 3080 10GB (10.0 GB VRAM) is the next step up.
