Your database isn’t slow. Your memory bus is choking.
AWS just dropped Amazon EC2 X8i instances with custom Intel Xeon 6. The headline number is kinda rude: up to 3.4x higher memory bandwidth than X2i. Max memory hits 6TB per instance. Translation: you stop paging to disk, tail latencies calm down, and in-memory stuff finally acts like… in-memory.
X8i is SAP-certified, pushes sustained all-core turbo at 3.9 GHz, and has always-on memory encryption. AWS benchmarks show chunky lifts: up to 50% higher SAPS, +47% PostgreSQL, +88% Memcached, and +46% AI inference. If you run SAP HANA, big Postgres, or hate cache misses, this is your fast lane.
In this guide, you’ll get the what, the why, and the ship-it plan. How to size, when to use EFA, how to tune IBC, and the cleanest migration path from X2i. Keep reading if you want fewer nodes, faster queries, and lower TCO.
You get the memory ceiling you’ve been waiting for. X8i scales to 6TB per instance (x8i.96xlarge and metal-96xl). It holds sustained all-core turbo clocks at 3.9 GHz. Memory bandwidth jumps up to 3.4x over X2i. That’s the choke point for most in-memory systems. X8i opens the pipe.
Security doesn’t tax your throughput. Always-on memory encryption is built-in. Pair it with sixth-generation AWS Nitro hardware for isolation and lower overhead. On larger sizes (48xlarge, 96xlarge, and their metal twins), you can enable Elastic Fabric Adapter (EFA). That gives low-latency, high-throughput traffic across distributed nodes.
Network caps at up to 100 Gbps, and EBS bandwidth peaks at 80 Gbps. With Instance Bandwidth Configuration (IBC), you can shift up to 25% between network and EBS. Pick based on what’s hot: replication traffic, log writes, or huge result sets.
Here’s how that bandwidth boost shows up day-to-day:
Most CPU problems are actually memory problems in disguise. Scaling Postgres reads? Running Memcached near its hit limit? Juggling SAP HANA column stores? X8i’s bandwidth keeps hot data on-die and out of swap hell.
A scenario you’ll recognize. You’ve got a 1.5TB working set. On X2i, P99 latencies stay high because the memory bus is the bottleneck. On X8i.24xlarge (1.5TB), the full dataset stays in memory. Cores get fed fast enough to flatten your latency curve. It’s not magic. It’s physics—bigger pipe, same workload.
Quick checklist to confirm you’re memory-bound, not CPU-bound:
If two or three ring true, X8i’s bandwidth and capacity are what you want.
AWS reports up to 50% higher SAP Application Performance Standard (SAPS) versus X2i. X8i is SAP-certified for mission-critical deployments. That matters because SAP HANA is both bandwidth-hungry and latency-sensitive. Net effect: simpler clusters with fewer nodes and less cross-node chatter.
Example you can model. You ran a 2-node HANA on X2i for memory headroom. X8i’s 6TB top end lets you consolidate to a single X8i.96xlarge. You keep performance without splitting. Fewer licenses, less inter-node coordination, tighter failover story.
Extra HANA notes to keep you out of trouble:
What this means for daily choices. Move hot tables into sharedbuffers more aggressively. Raise MAXMEMORY for Memcached. Pack more models per host for inference while meeting P99 SLAs.
A scenario to steal. A read-heavy Postgres analytics cluster sits at x8i.16xlarge (1TB). You profile buffer hits and see IO wait on checkpoints. Upgrade to x8i.32xlarge (2TB) to keep more working set resident. Use IBC to tilt 25% toward EBS during nightly compaction. Day dashboards get snappier. Night jobs stop thrashing.
PostgreSQL tuning playbook when moving to X8i:
Memcached tuning notes:
Inference stack patterns:
X8i rides sixth-generation AWS Nitro cards that offload virtualization, storage, and networking. You get lower jitter and more predictable throughput. That’s critical when micro-batching analytics or fanning out inference requests.
Networking tops out at 100 Gbps on the largest sizes. EFA support (48xlarge, 96xlarge, metal-48xl, metal-96xl) lets you scale distributed systems with low latency. If you’re doing EDA or complex simulations all day, EFA keeps nodes in lockstep.
On storage, EBS bandwidth hits 80 Gbps on top-end instances. That’s plenty for write-heavy logs, high-QPS OLTP with frequent checkpoints, or streaming checkpoints in data pipelines.
When to reach for EFA vs standard ENA:
Storage planning pointers:
IBC is the underrated feature here. You can bias the instance up to 25% toward network or EBS bandwidth. Think of it like a performance equalizer you set per workload phase.
Two practical plays:
Example setup. Your ML inference fleet on x8i.48xlarge serves spiky traffic. During day peaks, bias network to support fan-out to feature stores. At night, during backfills and compaction, bias EBS to stream writes at full speed. Same hardware, different dials.
How to decide IBC settings per phase:
You’ve got 14 sizes, from x8i.large (32 GiB) for dev to x8i.96xlarge (6,144 GiB) for big prod. Need bare metal? metal-48xl and metal-96xl skip the hypervisor for OS-level control or custom drivers.
Rule of thumb: size for memory headroom first. Then back into vCPU and bandwidth. If you’re over 85% memory used or see swap during peak, jump a tier. For SAP HANA, keep the full column store in memory plus a growth buffer.
Estimating your working set fast:
Because X8i does more work per node, you can consolidate. One 6TB node can replace two 3TB nodes. That means fewer EBS volumes, fewer replication hops, and lower cross-AZ traffic.
Play the consolidation game smartly:
Example move. A finance risk engine on X2i.32xlarge moves to X8i.32xlarge. You double memory bandwidth without changing topology. After cutover, P95 settles. Nightly risk recomputes finish 30–40% faster thanks to less page churn.
Preflight checklist before you flip the switch:
You’re getting roughly 43% higher overall performance on average versus X2i. Gains are bigger on memory-bound tasks. Memory bandwidth scales up to 3.4x, and max memory jumps to 6TB. If you hit memory ceilings or chase tail latency, this is a direct lift.
Where you’ll feel it most:
Compute-optimized like C8i is great for batch CPU-heavy work. But if your bottleneck is memory access, X8i is the right choice. Think HANA, in-memory analytics, caches, or inference with big embeddings. Also note: with IBC, X8i gives you knobs older gens don’t. You can keep the same fleet and retune as workload phases shift.
Example decision: a CI fleet compressing and compiling? Use C8i. A lookup service serving 100ms P99 from a 1TB keyspace? Use X8i.
Latency math you can feel:
They’re in US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Frankfurt) at launch. Spin them up via the AWS Management Console, CLI, or SDKs. Expect broader region rollout over time as capacity ramps.
More headroom and a fatter pipe. X8i delivers up to 43% higher overall performance. It has up to 3.4x higher memory bandwidth and scales to 6TB per instance. If you’re memory bound, you’ll see quick wins with fewer nodes.
Pick X8i if your graphs trend with memory metrics. Think cache hit rate, buffer pool residency, swap activity, and P95 under load. C8i crushes CPU-bound tasks like compiles and batch processing. X8i crushes memory-bound tasks.
Use bare metal if you need direct hardware access or specialized drivers. Or OS-level tweaks that benefit from skipping the hypervisor. Most apps will be fine on virtualized sizes thanks to Nitro offload.
IBC lets you bias up to 25% toward network or EBS bandwidth. Treat it like a phase knob. Push network during cross-node shuffles or inference fan-out. Push EBS during heavy ingest, compaction, or checkpointing. No code changes, just capacity tuning.
Yes. X8i is SAP-certified with up to 50% higher SAPS vs X2i in AWS benchmarks. You can run mission-critical SAP HANA and ERP with fewer nodes. You get simpler HA and steadier performance for monthly closes and reports.
In most cases, no. You’ll see gains by moving to X8i and retuning memory settings. Start with configuration before touching code.
Profile with perf or similar tools for stalled cycles per instruction. Track last-level cache misses, major page faults, and temp file usage for DBs. If better memory settings reduce tail latency, X8i will likely amplify the gains.
Yes. On the largest instances, watch thread placement. Keep processes and hot memory local to minimize cross-socket access. Check OS scheduler defaults and test.
Match volume types to your IO pattern. Scale parallel volumes for more throughput. Monitor queue depth and latency. Fast memory often exposes slow storage, so keep write paths unblocked.
Pro tip: make the first week a learning window. Watch CloudWatch metrics and app traces closely. Expect lower temp file usage, less IO wait, and flatter P99. Use those wins to justify consolidation and cost cuts.
You came here for faster results, not a hardware scavenger hunt. X8i’s pitch is simple: bigger memory, faster pipe, saner clusters. With Intel Xeon 6 and Nitro doing heavy lifting, you get predictable low-latency performance. Not just more cores. Start by sizing for memory headroom. Migrate cleanly with a shadow stack. Then use IBC and EFA to iron out bottlenecks. The payoff isn’t just nicer graphs. It’s fewer nodes, lower licensing, and happier SLAs.
Working with privacy-safe marketing analytics or clean room workloads on AWS? If Amazon Marketing Cloud is on your roadmap, explore AMC Cloud for managed, production-ready AMC pipelines. And if heavy AMC queries are the bottleneck, Requery can speed up SQL, cut compute costs, and keep SLAs tight.
“Bandwidth beats cleverness. When memory stops being the bottleneck, everything else suddenly looks well-architected.”