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RTX 4060 Ti 16GB LLM Calculator — What Can It Run?

Check which models fit on a RTX 4060 Ti 16GB: max parameters per precision, cache headroom and estimated tokens/sec.

Memory needed (GB)
Headroom on this card (GB)
Max params at this quant (B)
Est. decode speed (tok/s)

RTX 4060 Ti 16GB: 16 GB, 288 GB/s, ~22.1 TFLOPS FP16. The 4060 Ti 16GB pairs generous VRAM with an unusually narrow 128-bit bus (288 GB/s) — it FITS mid-size models that embarrass pricier cards, then generates noticeably slower than a 3090 on the same model.

Formula

needed = params × bpw ÷ 8 + reserve · fits if ≤ 16 GB · decode tok/s ≈ 0.6 × 288 GB/s ÷ weight-GB (bandwidth-bound)
References: NVIDIA/Apple official RTX 4060 Ti 16GB specifications; llama.cpp quantization size tables

About RTX 4060 Ti 16GB LLM Calculator — What Can It Run?

"Will it run?" is the first question of local AI, and for the RTX 4060 Ti 16GB this calculator answers it precisely: enter any model's parameter count and quantization and get the memory bill against this card's 16 GB, the largest model it can hold at that quant, and a bandwidth-derived decode-speed estimate (token generation streams the whole model per token, so 288 GB/s is the speed limit that matters). The 4060 Ti 16GB pairs generous VRAM with an unusually narrow 128-bit bus (288 GB/s) — it FITS mid-size models that embarrass pricier cards, then generates noticeably slower than a 3090 on the same model.

How to use RTX 4060 Ti 16GB LLM Calculator — What Can It Run?

  1. 1Enter your values into RTX 4060 Ti 16GB LLM Calculator — What Can It Run? — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
  2. 2The result recomputes live using the formula shown on the page; there is no button to press.
  3. 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.

Why use RTX 4060 Ti 16GB LLM Calculator — What Can It Run??

  • Computes RTX 4060 Ti 16GB LLM instantly in your browser — no sign-up, no upload, no server round-trip.
  • 100% free and unlimited, with the exact formula shown: needed = params × bpw ÷ 8 + reserve.
  • Runs entirely client-side, so every value you enter stays private on your device.
  • Live recompute as you type, with a worked example and authoritative references for trust.

Frequently asked questions

Why does my 4060 Ti generate slower than an older 3090?+

Token generation is memory-bandwidth-bound: every token streams the whole model from VRAM. The 3090 moves 936 GB/s versus the 4060 Ti's 288 GB/s — 3.2× — so the older card decodes roughly 3× faster despite similar capacity at 4-bit.

What models suit a 16 GB card best?+

FP16 7–8B models (~15 GB, tight), Q5/Q6 13–14B models (~10–12 GB) with healthy contexts, or Q4 20B-class models. It is also a sweet spot for Stable Diffusion XL and QLoRA fine-tuning of 7B models, where capacity beats bandwidth.

How is the tokens/sec estimate for the RTX 4060 Ti 16GB derived?+

Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 288 GB/s peak is achievable, matching llama.cpp benchmarks within ~20%. Prompt prefill is compute-bound and much faster per token.

Why reserve memory beyond the weights?+

The KV cache grows with context (use our per-model KV-cache calculators), CUDA/Metal runtimes take hundreds of MB, and allocator fragmentation wastes more. The default reserve suits 2–8K contexts; long-context work needs significantly more.

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