GeForce RTX 4060 Ti 16GB Price & Used Prices: Which Local LLMs Can It Run?

A single GeForce RTX 4060 Ti 16GB has 16GB of GDDR6 VRAM. At 4-bit quantization and 8K context it fits a dense model of up to about 23B parameters, or about 13B at 8-bit. MoE models can offload experts to system RAM to run larger ones, such as Qwen3.6 35B A3B (36B).

Not on a single card. At 4-bit with an 8K context a 70B dense model needs about 43GB, more than the GeForce RTX 4060 Ti 16GB's 16GB. It runs if you split the layers across 3 cards with llama.cpp (48GB total).

Among models released in 2026, the largest dense model that fits entirely in VRAM at 4-bit is Gemma 4 12B, at an estimated 28.2 tokens/s. The full list is in the table above.

Estimated from memory bandwidth at 4-bit and 8K context: 8B dense: about 38.6 tokens/s and 14B dense: about 22.8 tokens/s. MoE models read only the active parameters for each token, so they run much faster. Real-world results are usually 70%–100% of the estimate.

With vLLM tensor parallelism at 4-bit and 32K context, two GeForce RTX 4060 Ti 16GB cards cannot fit any model of 50B or larger released in 2026. At 4-bit with 32K context the minimum is 8 cards, which fits Qwen3.8 Flash Next.

As of Sep 29, 2026: eBay median $700 (5 listings), Amazon.jp median $1,327 (3 listings), and Xianyu median $537 (3 listings). Figures are medians of live listings, refreshed every 6 hours.

As of Sep 29, 2026, the Xianyu median is down 2.1% over 7 days, down 2.1% over 30 days.

The Tesla T10 has 16GB of VRAM and 403 GB/s of bandwidth; the GeForce RTX 4060 Ti 16GB has 16GB and 288 GB/s. At the Xianyu median, the Tesla T10 costs about $199, 63% less than the GeForce RTX 4060 Ti 16GB ($537). For 14B dense models at 4-bit, the GeForce RTX 4060 Ti 16GB is estimated at about 22.8 tokens/s and the Tesla T10 at about 31.7 tokens/s.