Vega 7nm or Vega 20 as AMD used to call this GPU on its roadmap a while ago, naturally increased the performance in deep learning and artificial intelligence but at the same time adding 32GB of HBM 2 made it too expensive to launch as a gaming part. This is a "we told you so" moment. The original Vega 64 and 56 only made profit as the market went crazy for any hardware suitable for cryptocurrency mining. Since this interest wave seems to be over, AMD could not bet on making let's say, a 16GB HBM 2 Vega 7nm gaming part, as the BOM would still be too expensive. David Wang SVP of Engineering at AMD, an ex-Synaptics chap who took over after Raja left has, went into a few more details about Vega 7nm.

Putting together bits of information dropped during AMD's PC-heavy hour-and-a-half presentation, it becomes apparent that Vega 7nm is finally aimed at high performance deep learning (DL) and machine learning (ML) applications – artificial intelligence (AI), in other words. AMD's EPYC successes may be paving the way for Vega 7nm in cloud AI training and inference applications. AMD claims that the 7nm process node it has co-developed with its fab partners will yield twice the transistor density, twice the power efficiency and about a third more performance than its 14nm process node. An educated guess says that not all Vega 7nm products will sport this high-end memory configuration – I think that showing off 32GB was a pointed message to AMD's cloud customers. AMD's Infinity Fabric interface will enable high bandwidth, coherent memory communications between Vega 7nm chips and AMD Zen processor chips, such as AMD's Zen2 7nm server chips.