ASUS Dual GeForce RTX 5060 Ti 16GB GDDR7 OC Edition

Hardware · NVIDIA

What AI models can the NVIDIA GeForce RTX 5060 Ti 16GB run?

VRAM
16.0 GB
Usable for models
16.0 GB
Memory bandwidth
448 GB/s
FP16 compute
23.7 TFLOPS
Released
2025
Launch price
$429
491 / 622 LLMs run Best: Gemma 4 12B 55 / 61 Image models run Best: FLUX 3 Image Klein 17 / 43 Video models run Best: LTX-Video 2B 68 / 70 Decision models run Best: Jina Reranker v3.5

More about this hardware

MakerNVIDIA
TypeGraphics card
InterfacePCIe 5.0

Apps that run models well on the NVIDIA GeForce RTX 5060 Ti 16GB

Windows

  1. OllamaOne command with Ollama’s own build of this model.
  2. LM StudioPoint-and-click: search, download and chat in one window.
  3. llama.cppUsually the first to support new model architectures.
  4. JanPoint-and-click: search, download and chat in one window.

Linux

  1. OllamaOne command with Ollama’s own build of this model.
  2. LM StudioPoint-and-click: search, download and chat in one window.
  3. llama.cppUsually the first to support new model architectures.
  4. JanPoint-and-click: search, download and chat in one window.

For a typical model that fits in memory. Each model page picks the best app for that model on this hardware. All apps

6 products with the NVIDIA GeForce RTX 5060 Ti 16GB

Best LLMs for the NVIDIA GeForce RTX 5060 Ti 16GB

Quality Model Quant Memory Speed Runs
80Great quality (A) · 80 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 12BGoogle Q5_K_M 10.6 GB 38.0 tok/s Runs great
79Great quality (A) · 79 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. K2 Horizon 7BInstitute of Foundation Models Q6_K 10.0 GB 42.4 tok/s Runs great
77Great quality (A) · 77 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 26B-A4BGoogle Q3_K_M 14.4 GB 116 tok/s Runs welltight fit
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. SenseNova Loop 9B PreviewSenseTime Q6_K 9.8 GB 39.5 tok/s Runs great
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. MiMo V2.6 Distill Qwen 9BXiaomi Q6_K 9.4 GB 40.5 tok/s Runs great
76Great quality (A) · 76 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.5 9BDeepReinforce Q6_K 9.6 GB 39.5 tok/s Runs great
75Great quality (A) · 75 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 9BAlibaba Q6_K 9.6 GB 39.5 tok/s Runs great
73Great quality (A) · 73 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Raon-1 mini Preview (30B-A4B)KRAFTON Q3_K_M 15.6 GB 108 tok/s Runs welltight fit
74Great quality (A) · 74 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.0 9BDeepReinforce Q6_K 9.6 GB 39.5 tok/s Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.8 27BAlibaba Q3_K_M 15.5 GB 25.6 tok/s Runs welltight fit
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.6 27BAlibaba Q3_K_M 15.5 GB 25.6 tok/s Runs welltight fit
70Great quality (A) · 70 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. gpt-oss 20BOpenAI MXFP4 13.5 GB 105 tok/s Runs great

Best Image models for the NVIDIA GeForce RTX 5060 Ti 16GB

Quality Model Quant Memory Speed Runs
89Excellent quality (A+) · 89 of 100: how capable the model is at FP8, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs FP8 12.4 GB 13 s Runs great
83Great quality (A) · 83 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Z-Image TurboAlibaba (Tongyi) Q8 GGUF 13.0 GB 7.3 s Runs great
84Great quality (A) · 84 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-Image TurboBaidu Q8 GGUF 15.0 GB 9.4 s Runs welltight fit
77Great quality (A) · 77 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs Q8 GGUF 10.5 GB 2.9 s Runs great
83Great quality (A) · 83 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 9BBlack Forest Labs Q8 GGUF 12.3 GB 5.5 s Runs greatencoder in RAM
80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. Z-ImageAlibaba (Tongyi) FP8 12.6 GB 39 s Runs well

Best Video models for the NVIDIA GeForce RTX 5060 Ti 16GB

Quality Model Quant Memory Speed Runs
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2BLightricks FP8 10.8 GB 57 s Runs great
44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 2BZhipu AI FP8 11.0 GB 2.3 min Runs great
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 T2V 1.3BAlibaba FP8 12.8 GB 9.2 min Runs well
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5BZhipu AI FP8 14.3 GB 6.9 min Runs welltight fit
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5B I2VZhipu AI FP8 14.9 GB 6.9 min Runs welltight fit

Best Decision models for the NVIDIA GeForce RTX 5060 Ti 16GB

Quality Model Quant Memory Speed Runs
86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3.5Jina AI F16 2.8 GB 27 ms Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 4BAlibaba Q8_0 6.0 GB 139 ms Runs well
84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Intern-Decision 4BShanghai AI Lab Q8_0 6.3 GB 88 ms Runs great
84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3Jina AI F16 2.8 GB 27 ms Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. zerank-2ZeroEntropy Q8_0 6.0 GB 139 ms Runs well
82Great quality (A) · 82 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. d1 3BLiquid AI F16 7.7 GB 75 ms Runs great
82Great quality (A) · 82 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. mxbai-rerank-large v2Mixedbread F16 4.5 GB 62 ms Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Shieldstral 1.0 3BMistral AI Q8_0 5.7 GB 133 ms Runs great

Best LLMs by task on this hardware Open in Model Finder

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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