What can an RTX 5060 Ti 16GB run?

The GeForce RTX 5060 Ti comes in two memory sizes: 16 GB and 8 GB of GDDR7, both on a 128-bit bus.^[1] This page is about the 16 GB card. The table below checks every language model in our calculator at three quantizations with an 8K-token context, using the same file sizes and fit rule as the rest of the site.

Each cell is the Hugging Face GGUF file size plus FP16 KV cache for an 8K-token context (or the model's maximum, if shorter). Fits = under 90% of 16 GB; Tight = 90 to 100%; Too big = over 16 GB. * = KV cache not included (no config data). Same data as the VRAM calculator and what can I run with 16 GB. Data checked 2026-10-30.

Check one model at your own context length in the VRAM calculator with the RTX 5060 Ti 16 GB selected.

How fast is it? Measured gpt-oss-20b

gpt-oss-20b is the best fit for this card in the table above, and llama.cpp's own gpt-oss guide includes a run on an RTX 5060 Ti 16GB (official MXFP4 GGUF, 11.27 GiB, flash attention on, whole model on the GPU):^[2]

That is far above reading speed for chat. Speeds for other cards and models are on the measured speed page.

What doesn't fit in 16 GB

The first models that miss are mid-size ones. Qwen3.8 27B needs about 18.0 GB at 4-bit with an 8K context (see it in the calculator), and the 31B to 35B models need about 19.6 to 22.5 GB. Your options:

• A 24 GB card. Everything in the table that says Too big at 4-bit up to about 21.6 GB fits a 24 GB card. See the RTX 3090 and RTX 4090 pages.
• Rent one for the hour. On Runpod Community Cloud, a 24 GB RTX A5000 listed at $0.16/hr and an RTX 3090 at $0.22/hr (read 2026-10-31). See GPU cost per hour for Vast.ai prices on the same cards.
• Offload to system RAM. MoE models such as Qwen3.6 35B-A3B can keep their experts in RAM: see expert offload. It is slower than running fully on the GPU; we don't publish a speed for it on this card because we haven't found a sourced number.

RTX 5060 Ti vs RTX 4060 Ti 16GB for local LLMs

Both have 16 GB on a 128-bit bus, so they fit exactly the same models: compare the table above with the RTX 4060 Ti 16GB page. The difference is memory speed. Specs from NVIDIA:^[1][3]

Generating text mostly reads the weights from memory, so the 5060 Ti's 56% more bandwidth is what matters. Reading a prompt is limited by compute, where the two are much closer. One person measured both cards with the same llama.cpp build and model:^[4]

The 4060 Ti in that test is the 8 GB version. NVIDIA lists the same 288 GB/s bandwidth for both 4060 Ti sizes, and this small model fits either one, so we expect the 16 GB card to be similar, but we haven't found a published 4060 Ti 16GB run of the same test. The lead column is our arithmetic.

In short: the 5060 Ti 16GB answers roughly 40% faster than a 4060 Ti in this test and reads prompts about 10% faster, but it doesn't fit anything the 4060 Ti 16GB can't. If you already own a 4060 Ti 16GB, the upgrade doesn't add any models. If you're buying, compare the two prices against that speed gap.

Don't buy the 8 GB version for LLMs

NVIDIA lists the RTX 5060 Ti 8 GB with the same specs and half the memory,^[1] so it fits far fewer models: gpt-oss-20b, the best fit above, needs about 11.9 GB at 4-bit and won't fit. Check the listing says 16 GB. If you have the 8 GB card, see what fits in 8 GB or the calculator with the 8 GB card selected.

Step up: RTX 5070 Ti

The RTX 5070 Ti also has 16 GB (same model list) but 896 GB/s of bandwidth, twice the 5060 Ti.^[1] In the same llama.cpp gpt-oss guide it generated gpt-oss-20b at 189.45 tok/s (about 70% faster) and read a 2,048-token prompt at 6,339.76 tok/s.^[2] Both 16 GB cards are compared on best GPUs for local LLMs: 16 GB.

More for 16 GB cards

• Everything that fits in 16 GB, including image generation and gaming
• RTX 4060 Ti 16GB fit table, the same list for the older card
• Run a MoE model bigger than your VRAM with expert offload to system RAM
• CUDA out of memory? What to change first

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Sources

All sources checked on 2026-11-02.

1. NVIDIA, GeForce RTX 5060 family specs (RTX 5060 Ti: 16 GB / 8 GB GDDR7, 128-bit, 4,608 CUDA cores, 180 W, 600 W system power) and the page's compare table (memory bandwidth: RTX 5060 Ti 448 GB/sec, RTX 5070 Ti 896 GB/sec, RTX 4060 Ti 288 GB/sec). Checked 2026-11-02. https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5060-family/
2. ggerganov, "guide: running gpt-oss with llama.cpp", llama.cpp GitHub discussion #15396: benchmarks on RTX 5060 Ti (16GB), build 9ef6b0b, and RTX 5070 Ti (16GB), build a094f38; -fa 1 -b 4096 -ub 2048. Checked 2026-11-02. https://github.com/ggml-org/llama.cpp/discussions/15396
3. NVIDIA, GeForce RTX 4060 family specs (RTX 4060 Ti: 16 GB or 8 GB GDDR6, 128-bit, 4,352 CUDA cores, 165 or 160 W, 550 W system power). Checked 2026-11-02. https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4060-4060ti/
4. llama.cpp GitHub discussion #15013, "Performance of llama.cpp on Nvidia CUDA": RTX 5060 Ti 16 GB and RTX 4060 Ti 8 GB results posted by @mike-llamacpp (Aug 2, 2025), build 89d10295, llama-bench -ngl 99 -fa 0,1. Checked 2026-11-02. https://github.com/ggml-org/llama.cpp/discussions/15013

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