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 2070 Super 8GB is a solid Turing-era card that introduced 2nd-gen Tensor Cores with INT8/INT4 acceleration to the mid-range. Its 8 GB VRAM fits 7B models at Q4 comfortably, and the 448 GB/s bandwidth is competitive with much newer 8 GB cards. Like all Turing GPUs, it lacks FP8 and BF16 support, and the 2nd-gen Tensor Cores are less efficient per core than Ampere or Ada. Still a viable secondary AI machine if you already own one.
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-capablelimited-vrambudget-friendlyhigh-bandwidth-for-class
Specifications
Compute
FP1618 TFLOPS
INT8144 TOPS
ArchitectureTuring
Memory
VRAM8 GB
Bandwidth448 GB/s
General
FamilyRTX 20
SegmentConsumer
InterconnectPCIe 3
Compute PlatformCUDA
MSRP$499
Key Features
CUDA Compute Capability 7.5 (Turing)2nd Gen Tensor Cores (INT8, INT4, FP16)448 GB/s memory bandwidth (GDDR6)18 TFLOPS FP16 computePCIe Gen 3 x16No FP8 or BF16 support
For AI Workloads
Strengths
- 448 GB/s bandwidth is strong for an 8 GB Turing card — fast decode on 7B models
- Tensor Cores enable meaningful INT8/INT4 acceleration via llama.cpp
- Cheaply available used
- Turing (compute 7.5) is still supported by major inference frameworks including vLLM
Considerations
- 8 GB VRAM limits to 7B models at Q4 — 13B is essentially out of reach
- No FP8 or BF16 — leaves efficiency gains on the table versus Ada and Ampere
- 2nd-gen Tensor Cores are less compute-efficient than Ampere 3rd-gen
- PCIe Gen 3 interface is a minor bottleneck on modern platforms
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 73.6 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 2070 Super 8GB
Upgrade paths
Upgrade from RTX 2070 Super 8GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
8
GB
RTX 2070 Super 8GBCategory AvgRTX 3080 10GB
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 | ~~1m 0s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~1m 42s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~2m 5s per image |
| Video Short (25f) | Won't fit | LTX Video 2B | ~~19.7s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~58s/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 71.4 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 73.6 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 | ~53.3s/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 2070 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.
