How does Elo rating gain scale with games played? I only take two players starting out at 1500 Elo and have them play against each other with one player winning all games. Also I leave out any artificially introduced cut-offs.

So the winner is constantly gaining rating while the other player is losing Elo rating. The gain is decreasing because the rating difference of the players increases.

How does (mathematically) the raiting gain scale with the number of games (n) played for large n?
It kind of depends exactly what you mean by artificial cutoffs. There is one explicitly stated artificial cutoff in the FIDE rating regulations. That says that if the rating difference is greater than 400 it will be treated as 400. But there is another hidden cutoff. The actual elo algorithm for calculating expected score or scoring probability involves power functions but FIDE provide a look-up table with approximations. This bottoms out when the rating difference is 735 with the expected score of the stronger player at 1.

So, if the 400 point cutoff is excluded but the exact FIDE algorithm with its table of expected scores is used then the maximum rating difference is 735 points and once the two players' ratings reach 2235 and 765 further wins for the stronger player will not change ratings. That is part of the reason the 400 point rule was introduced.

If, however, you are not talking about FIDE's implementation of the elo algorithms then the problem becomes a purely mathematical one. I look at the algorithms below.

The formulae for working out rating change for elo ratings work like this:

Suppose the initial ratings of the two players are r(1) and r(2)  and their recalculated ratings (which we want to work out) are r'(1) and r'(2).

First we need to calculate a power function for each initial rating to use to calculate the expected result of the match. So, call these power functions for the two players R(1) and R(2) and their expected scores E(1) and E(2).

So -
R(1) = 10^(r(1)/400) and R(2) = 10^(r(2)/400)

And -
E(1) = R(1) / (R(1) + R(2)) and E(2) = (1 - E(1)) = R(2) / (R(1) + R(2))

Now let S(1) = player 1's score in the game (going to be 1 in this case) and
let S(2) = player 2's score in the game (going to be 0 in this case) then

r'(1) = r(1) + k * (S(1) – E(1)) and
r'(2) = r(2) + K * (S(2) – E(2)) where k is the k factor

If there are no artificial cutoffs then the two ratings will eventually reach 3000 and 0 (with the same k factor for both, one player's rating gain is the other player's rating loss).

What happens in the next game?

Well r(1) = 3000 and r(2) = 0 so -
R(1) = 10^7.5 = 3162276.6 (to 8 sf) and
R(2) = 1
E(1) = 0.999999968
E(2) = 0.000000032, so
r'(2) = -0.000000032*k

The second player's rating continues to decrease and goes negative.

I'm fairly sure that as these algorithms are power function based the limits will be power function limits, i.e. the first player's rating will tend to infinity and the second's to -infinity