A vaguely related anecdote: working memory was one of the things that was damaged after my stroke; for a while afterwards I was incapable of remembering more than two or three items when asked to repeat a list. I wasn't exactly stupider than I am now, but I was something pretty similar to stupid. I couldn't understand complex arguments, I couldn't solve logic puzzles that required a level of indirection, I would often lose track of the topic of a sentence halfway through.

Of course, there was other brain damage as well, so it's hard to say what causes what, and the plural of anecdote is not data. But subjectively it certainly felt like the thing that was improving as I recovered was my ability to hold things in memory... not so much number of items, as reliability of the buffers at all. I often had the thought as I recovered that if I could somehow keep improving my working memory -- again, not so much "add slots" but make the whole framework more reliable -- I would end up cleverer than I started out.

Take it for what it's worth.

It would appear that all of us have very similar amounts of working memory space. It gets very complicated very fast, and there are some aspects that vary a lot. But in general, its capacity appears to be the bottleneck of fluid intelligence (and a lot of crystallized intelligence might be, in fact, learned adaptations for getting around this bottleneck).

How superior would it be? There are some strong indication that adding more "chunks" to the working space would be somewhat akin to adding more qubits to a quantum computer: if having four "chunks" (one of the most popular estimates for an average young adult) gives you 2^4 units of fluid intelligence, adding one more would increase your intelligence to 2^5 units. The implications seem clear.

Although the exact relationship isn't known, there's a strong connection between IQ and working memory - apparently both in humans and animals. E.g. Matzel & Kolata 2010:

Accumulating evidence indicates that the storage and processing capabilities of the human working memory system co-vary with individuals’ performance on a wide range of cognitive tasks. The ubiquitous nature of this relationship suggests that variations in these processes may underlie individual differences in intelligence. Here we briefly review relevant data which supports this view. Furthermore, we emphasize an emerging literature describing a trait in genetically heterogeneous mice that is quantitatively and qualitatively analogous to general intelligence (g) in humans. As in humans, this animal analog of g co-varies with individual differences in both storage and processing components of the working memory system. Absent some of the complications associated with work with human subjects (e.g., phonological processing), this work with laboratory animals has provided an opportunity to assess otherwise intractable hypotheses. For instance, it has been possible in animals to manipulate individual aspects of the working memory system (e.g., selective attention), and to observe causal relationships between these variables and the expression of general cognitive abilities. This work with laboratory animals has coincided with human imaging studies (briefly reviewed here) which suggest that common brain structures (e.g., prefrontal cortex) mediate the efficacy of selective attention and the performance of individuals on intelligence test batteries. In total, this evidence suggests an evolutionary conservation of the processes that co-vary with and/or regulate “intelligence” and provides a framework for promoting these abilities in both young and old animals.

or Oberauer et al. 2005:

Hence, we might conclude—setting aside the above mentioned caveats for such analyses—that [Working Memory Capacity] and g share the largest part of their variance (72%) but are not identical. [...] Our methodological critique notwithstanding, we believe that Ackerman et al. (2005) are right in claiming that WMC is not the same as g or as gf or as reasoning ability. Our argument for a distinction between these constructs does not hinge on the size of the correlation but on a qualitative difference: On the side of intelligence, there is a clear factorial distinction between verbal and numerical abilities (e.g., Su¨ß et al., 2002); on the side of WMC, tasks with verbal contents and tasks with numerical contents invariably load on the same factor (Kyllonen & Christal, 1990; Oberauer et al., 2000). This mismatch between WMC and intelligence constructs not only reveals that they must not be identified but also provides a hint as to what makes them different. We think that verbal reasoning differs from numerical reasoning in terms of the knowledge structures on which they are based: Verbal reasoning involves syntax and semantic relations between natural concepts, whereas numerical reasoning involves knowledge of mathematical concepts. WMC, in contrast, does not rely on conceptual structures; it is a part of the architecture that provides cognitive functions independent of the knowledge to which they are applied. Tasks used to measure WMC reflect this assumption in that researchers minimize their demand on knowledge, although they are bound to never fully succeed in that regard. Still, the minimization works well enough to allow verbal and numerical WM tasks to load substantially on a common factor. This suggests that WMC tests come closer to measuring a feature of the cognitive architecture than do intelligence tests.