The outlier rule is based on the inter-quartile range (upper minus lower quartile). 

**Your data.** If you have so many RAM values at 4 and 8 that those are the lower and upper quartiles, respectively, then $\text{IQR} = 8 - 4 = 4,$ and any value above $Q_3 + 1.5(\text{IQR}) = 8 + 1.5(4) = 14$ will show as a high outlier. A small-sample version follows:

    x = c(2,2,4,4,4,4,4,4,8,8,8,8,8,8,8,8,16,16,16,24,24)
    summary(x)
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      2.000   4.000   8.000   8.952   8.000  24.000 
    IQR(x)
    [1] 4

    boxplot(x, horizontal=T, col="skyblue2", pch=19)

[![enter image description here][1]][1] 

If you take logs of your observations, a boxplot may be somewhat better suited as a graphical description.

    y = log2(x)
    summary(y)
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      1.000   2.000   3.000   2.818   3.000   4.585 
    IQR(y)
    [1] 1
    boxplot(y, horizontal=T, col="skyblue2", pch=19)

 [![enter image description here][2]][2]

**Outliers are common in exponential data.** It is a characteristic of samples from right-skewed distributions to show numerous 'outliers'.  Below are
boxplots for 20 samples of size $n = 100$ from an exponential distribution with mean 10. (About 99% of such samples will show at least one outlier.)

    m = 20;  n = 100;  x = rexp(m*n, .1);  g = rep(1:20, each=100)
    boxplot(x ~ g, col="skyblue2", pch=19)

[![enter image description here][3]][3]

**Outliers are not rare in normal data.** Moreover, slightly more than half of normal samples of size $n = 100$ show at least one outlier.

    set.seed(606)
    nr.out = replicate(10^5,
              length(boxplot.stats(rnorm(100, 50, 7))$out))
    mean(nr.out >= 1)
    [1] 0.52505
        nr.out
          0       1       2       3       4       5       6       7 
    0.47495 0.28644 0.13589 0.06059 0.02475 0.01010 0.00439 0.00171 
          8       9      10      11      12      13 
    0.00073 0.00027 0.00007 0.00006 0.00004 0.00001 

Boxplots for 20 of the 100,000 normal samples from this simulation are shown below.

[![enter image description here][4]][4] 

_Note:_ Applied to a normal _population_ the outlier rule would label observations more than about 2.7 SDs from the mean
as outliers. _Samples_ do not precisely emulate populations, but normal tails have enough probability that it is not rare for moderately large samples to have some outliers. Boxplot 'outliers' are worth a second
look, but they are by no means necessarily 'errors'.

    qnorm(.75) + 1.5*diff(qnorm(c(.25,.75)))
    [1] 2.697959
    2*pnorm(-2.7)
    [1] 0.006933948


  [1]: https://i.sstatic.net/E7CNt.png
  [2]: https://i.sstatic.net/F5tph.png
  [3]: https://i.sstatic.net/iUHZR.png
  [4]: https://i.sstatic.net/TNkYd.png