Pet peeve: eventual consistency isn't for scalability and performance, it's for availability. In a well designed system, the whole debate only matters during in a failure condition: a strongly consistent system gives up availability upon a certain kind of failure, an eventually consistent system gives up consistency upon a certain kind of failure.

There are strongly consistent scalable "NoSQL" systems e.g., BigTable. Megastore even provides complex distributed cross row transactions.

In a well tuned system, loss of availability (in a failure scenario) could be minimize to seconds. What this means for performance is that you have systems which encounter second-long latency spikes upon failures.

It also isn't a binary switch:

1) Quorums can be used to achieve read-your-write consistency in the case of simple failures (loss of 1 node out of 3).

2) There's multiple kinds of relaxed consistency models. One of them is serializable consistency: you may get a stale read, but the the order of reads is the same as the order of writes. This is used by PNUTs and can be achieved by serializing the writes through a single master. This means that there's (again, for a short time period) loss of write availability in a failure, but there's no loss of read availability.

3) Paxos/multi-Paxos can be used to achieve atomic writes (all available nodes receive the write) while withstanding simple failures (similar to quorum protocols... and I believe multi-Paxos uses quorum protocols under the cover to improve liveness over "raw" Paxos). This is at the cost of higher latency (and complexity). [Edit: In this case, you're dealing with full-blown strong consistency, but with -- at the cost of latency -- the ability to tolerate certain kinds of simple failures/trivial partitions]

Clustrix looks interesting, but it looks like it addresses the scalability and performance issues with RDBMS, but not the availability feature. If an RDBMS were to drop the "A" and "I" in ACID (C in "ACID" means serializable view of the execution, which is not the same as the C in "CAP": the latter means all nodes in a cluster agreeing upon what the data is which isn't required for the former), it would be possible to build a highly available, low latency RDBMS; but it would also not be as useful without atomic and isolated cross row transactions.

[Disclaimer: I work on a Dynamo-style database, but fond of PNUTs as an architecture (more difficult to implement, but IMO a better fit for plurality of web applications) and generally fascinated and interested in distributed systems, databases and systems programming in general]