Update README.md

README.md

@@ -14,7 +14,7 @@ It learns users' preferences by capturing the short-term and sequential patterns
 VMIS-kNN is an index-based variant of a state-of-the-art nearest neighbor algorithm to session-based recommendation, which scales to use cases with hundreds of millions of clicks to search through.
 
 The VMIS-kNN implementation has a p90 prediction latency of <1.7ms in our micro benchmarks on private and public trainingsets up to 60M user-item interactions with 1.76 million distinct items. 
-The Serenade recommender service using the VMIS-kNN algorithm easily handles 1000 predictions per second using only two vCPU's in total. The p90 prediction latency of the deployed system with kubernetes is < 7ms end-to-end, measured from a different node using a http client, including http overhead, network traffic, istio loadbalancers, fetching session information from RocksDb and filtering for product availablity and intimacy, the serializing of the results etc. Training data is 2.3 billion records of which 582 million training records are used after index pruning and contains about 6.5 million distinct products.
+The Serenade recommender service using the VMIS-kNN algorithm easily handles 1000 predictions per second using only two vCPU's in total. The p90 prediction latency of the deployed system with kubernetes is < 7ms end-to-end, measured from a different node using a http client, including http overhead, network traffic, istio loadbalancers, fetching session information from RocksDb and filtering for product availablity and intimacy, the serializing of the results etc. Training data is 2.3 billion records of which 582 million training records are used after index pruning and contains about 6.5 million distinct products. The index requires about 11GB of memory per serving node.
 
 # Quick guide: getting started with Serenade.