Can an RTX 4060 Ti 16GB run Qwen3 8B?

In practice

Qwen3 8B sits comfortably on RTX 4060 Ti 16GB. At Q8_0 it takes 9.02 GB of the 15.2 GB available, which leaves 6.18 GB spare, enough for the 8k window we assumed and for a window of up to about 32k tokens before memory runs out. Generation runs at about 24.6 tokens per second, faster than most people read, so a 500-token answer takes about 20 seconds.

Of the memory in use, 83% is the weights and 13% is the attention cache. There is no higher-quality file to step up to, so the spare memory is best spent on a longer window.

Reading a 4,000-token prompt, roughly a long email or a few pages of text, takes about 2 seconds before the first word appears. That part is limited by compute and not by bandwidth, so it follows a different spec from the writing speed.

The numbers

Qwen3 8B has 8.19 billion parameters across 36 layers. The GeForce RTX 4060 Ti 16GB has 16 GB of VRAM at 288 GB/s, of which about 15.2 GB is left for a model once the operating system has taken its share.

9 of the 9 levels fit entirely. Levels marked degraded are listed for completeness, not as advice.

How long a conversation it holds

Memory rises with the length of the conversation because the attention cache keeps a key and a value for every token. At Q8_0, the longest window that still fits on this device is 32k tokens. Setting the window to what you need and no more is the cheapest way to buy back memory.

The settings we would use

llama-server -m <model>.gguf -ngl all -c 8192 -fa on

In Ollama, set OLLAMA_CONTEXT_LENGTH to the window you want, run ollama ps while the model is loaded and check that the processor column reads 100% GPU. If it shows a split on a model that fits, the Ollama guide shows how to fix it. The llama.cpp flags guide explains each option above.

What else an RTX 4060 Ti 16GB runs well

For comparison, these are the other popular models that fit this device at a sound quantization, best pairings first.

The same model on nearby hardware

Memory decides whether Qwen3 8B loads and bandwidth decides how quickly it answers. Here is what a step down, a card with the same memory and a step up do with it.

Wait for the first word251 ms

Then writes at24.6 tok/s

Whole answer7.3 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Questions people ask

Keep going

Everything the GeForce RTX 4060 Ti 16GB runs, the full breakdown for Qwen3 8B, how the numbers are made in the methodology, or a different pairing in the calculator.