Uninformed Search Techniques
Breadth-First Search needs to store a frontier of nodes to visit next (where "visit" basically means: see if it's the goal, generate its children and add to frontier otherwise). You can visualize the memory requirements of this as a pyramid; initially you just have the root node in there. As the search process continues and you keep going further down, it will become wider and wider (more memory required). The amount of memory required in the worst case to find a node at depth d can be expressed as O(b^d), where b is the branching factor (see details here). Intuitively, whenever you go one level deeper, your memory requirements multiply by a factor of b (your pyramid becomes b times wider; actually this means pyramid is not quite the right shape, it should rapidly curve outwards and become wider much faster than a triangle/pyramid would).
Depth-First Search also stores a frontier (or stack in the case of DFS) of nodes to visit next. However, in the case of DFS this generally grows less quickly. Whenever you visit a node (pop it off of the stack), you generate all of its children and push them on top of the stack. However, whereas BFS would then continue "to the right" by visiting a relatively "old" node and generating all of its children again, DFS continues by going "down" and visiting one of the children that only just got pushed onto the stack. Once DFS has finished a part of the search tree (you can visualize this in your head as DFS having completed searched for example the left half of the pyramid), that entire section no longer needs to be stored in memory. Assuming you have a search tree of finite depth d, the worst case space complexity is O(bd) (you have to search one path all the way down to a level of d, and for each of those levels have b nodes in the stack).
Note that, based on the above, it is not sufficient to characterize your comparison as "uninformed" vs "informed" search. We've only just looked at uninformed search techniques, and already have two different memory requirements.
In the above, I also did not consider subtleties such as the question of whether or not you're additionally memorizing which nodes of a graph you have already previously visited so that you can avoid visiting them again. This is not necessary in a tree, but may be necessary in a graph with cycles.
Informed Search Techniques
A* is probably one of the most canonical examples of informed search algorithms. In terms of worst case memory requirements, it's really similar to BFS; it also stores a frontier of nodes to visit next, but prioritizes those based on some estimate of "goodness" rather than Breadth-First order. In cases where you the information you're using (typically a combination of known/incurred costs + heuristic estimate of future costs) is of extremely poor quality, this can regress to the level of Breadth-First Search, so the worst-case space complexity is the same as that for BFS (see details). In practice, if you have high quality information (good heuristics), the memory requirements will be much better though; in the case of an ideal/perfect heuristic, your search algorithm will pretty much go directly to the goal and hardly require any memory.
There is also an algorithm called Iterative Deepening A* which has a much better space complexity, at the cost of often being slower. It is still an informed search algorithm using exactly the same kind of information as A* though. So, really, memory requirements cannot be characterized in terms of informed vs. uninformed search; in both cases there are algorithms that require a lot of memory, and algorithms that require less memory.