GeForce RTX 4070 Ti SUPER AD102

The GeForce RTX 4070 Ti SUPER AD102 is an NVIDIA GPU built on the Ada Lovelace architecture, released 2024-06-10. For running AI locally, the numbers that matter are its 16 GB of GDDR6X and 672.3 GB/s of memory bandwidth. VRAM decides which models fit at all; bandwidth sets how fast they generate text.

What you can run on 16 GB

At Q4_K_M quantization (the usual local default), 16 GB holds models up to roughly 25B parameters, leaving headroom for context. On this card you can run, among others:

• gpt-oss-20b - 21.5B parameters
• Qwen2.5-14B-Instruct - 14.8B parameters
• Qwen2.5-Coder-14B-Instruct - 14.8B parameters
• Phi-4 - 14B parameters
• Qwen3 14B - 14B parameters

Larger models need a higher-VRAM card, a second GPU, or CPU offload (which is much slower). Check any specific model with the VRAM calculator, or see the full picture on what can I run.

Local LLM speed (LLaMA 3, llama.cpp)

Single-stream token-generation throughput - estimated from memory bandwidth:

Model (quant)	Speed on GeForce RTX 4070 Ti SUPER AD102
Llama 3 8B (Q4_K_M)	78.7 tok/s
Llama 3 8B (F16)	✗ won't fit
Llama 3 70B (Q4_K_M)	✗ won't fit

Because decode is memory-bandwidth bound, the 672.3 GB/s figure is the best single predictor of chat speed on this card. Estimates are calibrated against measured RTX-40-series cards and are typically within ~15%.

Memory and power

• VRAM: 16 GB GDDR6X (256-bit bus)
• Bandwidth: 672.3 GB/s
• TDP: 285 W - a 600 W+ power supply is recommended
• Process: 5 nm
• Interface: PCIe 4.0 x16

Quantization and context

Quantization trades a little quality for a lot of VRAM. On 16 GB you can fit roughly a 25B model at Q4_K_M, about a 13B model at the higher-quality Q8, or a smaller model at full FP16. Longer context windows also consume VRAM (the KV cache grows with context length), so leave a few GB of headroom if you plan to use large prompts or many concurrent requests. For most chat and coding use, Q4_K_M on this card is the sweet spot between speed, quality, and the 16 GB budget.

How it compares

Similar cards for local AI, by VRAM and 8B-Q4 speed:

GPU	VRAM	Bandwidth	Llama 3 8B Q4
GeForce RTX 4070 Ti SUPER AD102	16 GB	672.3 GB/s	78.7 tok/s
AMD Radeon RX 7600 XT	16 GB	288 GB/s	33.7 tok/s
AMD Radeon RX 7800 XT	16 GB	624 GB/s	73 tok/s
AMD Radeon RX 9070	16 GB	512 GB/s	59.9 tok/s

Bottom line

The GeForce RTX 4070 Ti SUPER AD102 is best for llm-inference, image-gen. Its 16 GB suits 7B-14B models and image generation well. If you need more, compare with AMD Radeon RX 7600 XT and AMD Radeon RX 7800 XT.

Sources

• Specifications: RightNow GPU Database (TechPowerUp data)
• Benchmarks: GPU-Benchmarks-on-LLM-Inference (basis for the bandwidth estimate)

Specs and benchmarks last checked 2026-06-08. Verify current pricing before buying.