NVIDIA RTX 5060 Ti 8GB

NVIDIA RTX 5060 Ti 8GB: NVIDIA launched the RTX 5060 Ti in two VRAM configurations on the same day, April 16, 2025: this 8GB card at $379 and a 16GB sibling at $429, both cut from the identical GB206-300 die with 4,608 CUDA cores, 144 Tensor cores, and 36 RT cores (NVIDIA's own GeForce RTX 5060 family spec page; Wikipedia's GeForce RTX 50 series table confirms every figure here). Both variants share the exact same 448 GB/s bandwidth on a 128-bit bus, so the extra capacity on the 16GB card buys larger models, not faster ones. TechPowerUp's review of this 8GB card was blunt about the memory ceiling, titling it "So Many Compromises," and GamersNexus's own RTX 5060 Ti launch review ("More Marketing BS") measured a real 13-27% average gain over the RTX 4060 Ti at 1440p on the 16GB card, a generational improvement this 8GB card shares since both configurations run the same GB206-300 die at the same clocks.

Because this card shares its VRAM capacity and memory bandwidth exactly with the plain RTX 5060 (8GB, 448 GB/s), this site's calculator returns bit-for-bit identical results between them for every one of the 86 models this site tracks, despite the Ti card's 768 more CUDA cores (4,608 vs 3,840) and $80 higher price: Llama 3.1 8B decodes at 57.4 tok/s at NVFP4 (5.68 GB) on both, and Qwen 2.5 7B decodes at 68.2 tok/s at NVFP4 (4.78 GB) on both. The 16GB RTX 5060 Ti sibling draws the real dividing line: 28 of the 84 models this site tracks with a standard quant ladder reach a fits verdict on the 16GB card's VRAM but only reach offload on this 8GB card at the identical quant, including Qwen 3.6 27B (offload at 3.8 tok/s here versus 21.5 tok/s natively there) and GPT-OSS 20B, a model OpenAI explicitly sized for a 16GB card (9.9 tok/s offloaded here versus 46.9 tok/s natively there).

Full CUDA support, but like every desktop Blackwell card this reports compute capability 12.0 (sm_120), genuinely new hardware needing the CUDA 12.8 runtime; NVIDIA's own engineering blog measured a roughly 27% LM Studio/llama.cpp speedup on the sibling RTX 5080 just from that runtime upgrade, and a stale llama.cpp, Ollama, or PyTorch build may still not recognize this GPU. This card can also run NVFP4 in hardware, and it's the fastest fit for both reference models above: Llama 3.1 8B and Qwen 2.5 7B each reach their best tok/s at NVFP4 rather than the legacy GGUF K-quants. The buying decision this card actually poses is whether the Ti name and 768 extra CUDA cores are worth $80 over the plain RTX 5060: for local LLM inference specifically, this site's calculator can't tell the two apart at all, so that premium buys gaming performance and general compute headroom, not VRAM capacity or decode speed. Reviewers were broadly critical of 8GB at this card's $379 price point; stepping up to the 16GB sibling for $50 more is the only way in this family to actually change which models fit.