1.5 TB of RAM. Every byte yours.
Purpose-built for in-memory databases, real-time analytics, and large caching tiers. DDR5 ECC RAM up to 1.5 TB on a single bare-metal node — no swapping, no NUMA inefficiency, no contention.
When RAM is your most critical resource
1.5 TB of DDR5 ECC memory on a single dedicated node — no virtualisation, no memory ballooning, no NUMA misconfiguration.
DDR5 operating at up to 5600 MT/s with ECC on every DIMM. Load entire PostgreSQL databases, Elasticsearch indices, or Redis datasets entirely in memory — zero disk I/O on hot paths.
Local DDR5 RAM delivers sub-80 ns access latency — orders of magnitude faster than any NVMe or network storage. Critical for in-memory databases, stream processors, and real-time fraud detection.
Single-error correction, double-error detection (SECDED) ECC on all DIMMs. Essential for financial and healthcare workloads where silent data corruption is unacceptable. Memory errors are logged and reported via IPMI.
Configured with an 8:1 GB-to-core ratio (e.g., 1,536 GB RAM / 192 cores) to ensure your in-memory workloads aren't CPU-bound. More memory per thread means fewer cache evictions and higher hit rates.
All High-Memory servers come with a 10 Gbps uplink for high-throughput replication, bulk data ingestion, and low-latency read traffic. Private VLAN available for cluster interconnects.
Local NVMe drives provide fast persistence for write-ahead logs, RDB snapshots, and overflow. When RAM isn't enough, NVMe is 10× faster than network-attached storage for overflow and swap.
High-Memory configurations
From 192 GB to 1.5 TB of DDR5 ECC RAM on a single node. All include NUMA topology, ECC logging, and IPMI out-of-band access.
Who runs on High-Memory servers
Run Redis, Memcached, or SAP HANA with the full dataset in RAM
When your entire working dataset fits in memory, query latency drops from milliseconds to microseconds. High-Memory servers let you run Redis clusters, Memcached pools, or SAP HANA in-memory analytics without the cost of cloud in-memory tiers.
Sub-second BI queries over billion-row datasets
ClickHouse, Apache Druid, and StarRocks dramatically outperform when data fits in memory. With 1.5 TB RAM, you can keep months of time-series data hot — enabling dashboard queries that return in under 100 ms.
Stop tuning heap limits — give your JVM all the memory it needs
Elasticsearch, Kafka brokers, Hadoop NameNodes, and large Spring Boot applications all benefit from massive heap sizes. High-Memory servers eliminate the root cause of OutOfMemoryError crashes and GC pause storms.
Frequently asked questions
1.5 TB of RAM. All yours.
The only way to get true in-memory performance is to own the hardware. No memory ballooning, no balloon drivers, no memory overcommit — just raw DDR5 bandwidth at your disposal.