The Knapsack problem is one of the most fundamental NP-complete problems at the intersection of computer science, optimization, and operations research. A recent line of research worked towards understanding the complexity of pseudopolynomial-time algorithms for Knapsack parameterized by the maximum item weight $w_{\mathrm{max}}$ and the number of items $n$. A conditional lower bound rules out that Knapsack can be solved in time $O((n+w_{\mathrm{max}})^{2-δ})$ for any $δ> 0$ [Cygan, Mucha, Wegrzycki, Wlodarczyk'17, Künnemann, Paturi, Schneider'17]. This raised the question whether Knapsack can be solved in time $\tilde O((n+w_{\mathrm{max}})^2)$. This was open both for 0-1-Knapsack (where each item can be picked at most once) and Bounded Knapsack (where each item comes with a multiplicity). The quest of resolving this question lead to algorithms that solve Bounded Knapsack in time $\tilde O(n^3 w_{\mathrm{max}}^2)$ [Tamir'09], $\tilde O(n^2 w_{\mathrm{max}}^2)$ and $\tilde O(n w_{\mathrm{max}}^3)$ [Bateni, Hajiaghayi, Seddighin, Stein'18], $O(n^2 w_{\mathrm{max}}^2)$ and $\tilde O(n w_{\mathrm{max}}^2)$ [Eisenbrand and Weismantel'18], $O(n + w_{\mathrm{max}}^3)$ [Polak, Rohwedder, Wegrzycki'21], and very recently $\tilde O(n + w_{\mathrm{max}}^{12/5})$ [Chen, Lian, Mao, Zhang'23]. In this paper we resolve this question by designing an algorithm for Bounded Knapsack with running time $\tilde O(n + w_{\mathrm{max}}^2)$, which is conditionally near-optimal. This resolves the question both for the classic 0-1-Knapsack problem and for the Bounded Knapsack problem.