Amazon EC2 X8i is Generally Available: Custom Intel Xeon 6, Up to 6TB RAM, and Why Memory Bandwidth Suddenly Matters Again

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AWS has a long tradition of releasing EC2 instance families that sound like they were named by a committee with a Scrabble addiction. But every now and then, the letters actually signal something meaningful: a shift in what the cloud is optimizing for.

This week’s example is the new Amazon EC2 X8i family, now generally available for memory-intensive workloads, and built on custom Intel Xeon 6 processors that—importantly—are “available only on AWS.” citeturn0search1turn0search0turn1search2

The original announcement comes from the AWS News Blog, authored by Channy Yun. citeturn0search1

Below is the expanded story: what X8i is, why AWS is leaning hard into memory bandwidth, how this compares to prior memory-optimized generations, and what it means for SAP HANA, large databases, analytics, EDA, and even AI inference (yes, inference can be memory hungry too—your model weights have to live somewhere).

What AWS Actually Announced (and on Which Date)

AWS announced the general availability of EC2 X8i instances on January 15, 2026. These are memory-optimized instances designed for workloads like SAP HANA, “traditional large-scale databases,” analytics, and EDA. citeturn0search0turn0search1

This matters because X8i isn’t just “the next thing after X2i.” AWS is positioning it as a fairly sharp step forward, calling out:

  • Up to 1.5x more memory capacity (up to 6TB) compared with X2i. citeturn0search0turn0search1turn1search2
  • Up to 3.4x more memory bandwidth versus X2i. citeturn0search0turn0search1turn1search2
  • Up to 43% higher performance overall versus X2i, with larger uplift on some benchmarks. citeturn0search0turn0search1turn1search2

Also notable: X8i is described as SAP-certified and includes two bare-metal sizes alongside the usual virtualized sizes. citeturn0search0turn0search1turn1search2

The Headline Specs: X8i Sizes, Network, and EBS Bandwidth

EC2 X8i comes in 14 sizes, ranging from x8i.large (2 vCPUs, 32 GiB) up to x8i.96xlarge (384 vCPUs, 6,144 GiB), plus bare-metal variants. citeturn0search1turn1search2

AWS highlights up to 100 Gbps networking, Elastic Fabric Adapter (EFA) support, and up to 80 Gbps of Amazon EBS throughput depending on size. citeturn0search1turn1search4

For convenience, here’s a condensed view of what “scale-up” looks like in this family:

  • x8i.16xlarge: 64 vCPU, 1,024 GiB RAM, 30 Gbps network, 20 Gbps EBS citeturn0search1turn1search2
  • x8i.32xlarge: 128 vCPU, 2,048 GiB RAM, 50 Gbps network, 40 Gbps EBS citeturn0search1turn1search2
  • x8i.64xlarge: 256 vCPU, 4,096 GiB RAM, 80 Gbps network, 70 Gbps EBS citeturn0search1turn1search2
  • x8i.96xlarge: 384 vCPU, 6,144 GiB RAM, 100 Gbps network, 80 Gbps EBS citeturn0search1turn1search2

In practical terms, these numbers are AWS telling you: “Yes, you can run gigantic shared-memory things and still feed them storage and network at a pace that doesn’t make your expensive CPU cores twiddle their thumbs.” Which brings us to the real story: memory bandwidth.

Why Memory Bandwidth Is the Real Star of the Show

If you’ve ever tuned a database, you know the painful truth: adding CPU cores does not magically make I/O or memory faster. Modern servers often have plenty of compute, but performance gets capped by how quickly they can move data in and out of memory (and how efficiently caches and NUMA domains behave under load).

AWS is explicitly advertising up to 3.4x more memory bandwidth compared to X2i. citeturn0search0turn0search1turn1search2

That’s a big claim, and it helps explain why the processor detail AWS chose to highlight isn’t “more cores” but “custom Intel Xeon 6 with a sustained all-core turbo frequency of 3.9 GHz.” citeturn0search1turn1search2

Memory bandwidth, explained without a whiteboard

Think of your CPU cores as a team of developers and memory bandwidth as your office Wi‑Fi. You can hire more developers (cores), but if the Wi‑Fi (memory subsystem) is congested, everyone waits for downloads. The sprint still fails, just with more people complaining on Slack.

In real systems—especially in-memory databases and analytics—performance is often gated by:

  • How fast data pages can be scanned, joined, aggregated, and shuffled in memory
  • How predictable latency is when NUMA locality isn’t perfect
  • How much “headroom” exists before the system starts thrashing caches and memory controllers

So, when AWS claims 3.4x more memory bandwidth versus X2i, it’s effectively saying: “This is not just a bigger RAM ceiling; it’s a wider on-ramp.” citeturn0search1turn1search2

Custom Intel Xeon 6 “Only on AWS”: What That Likely Means

AWS states X8i is powered by custom Intel Xeon 6 processors “available only on AWS.” citeturn0search0turn0search1turn1search2

AWS doesn’t publish full silicon disclosure (and that’s not unusual in cloud land), but “custom” in hyperscaler speak typically implies some combination of:

  • Specific binning targets (frequency/power envelopes tuned for data center efficiency)
  • Platform tuning for memory channels, DIMM configurations, or firmware knobs
  • Security features and telemetry hooks aligned with the AWS Nitro architecture

We have at least one additional breadcrumb from industry reporting around AWS’s use of custom Xeon 6 in other instance families: TechRadar noted AWS talking about DDR5 7200 MT/s memory and up to 3.9 GHz all-core turbo in Xeon 6-based instances, framing it as a meaningful Intel win in the cloud. citeturn1news12

The larger context is that CPU differentiation in cloud is no longer just “Intel vs AMD vs Arm.” It’s “which vendor can give AWS the exact mix of performance-per-watt, memory throughput, and platform features that fit AWS’s very opinionated fleet design.”

How X8i Compares to the Previous Generation (X2i and Friends)

AWS repeatedly benchmarks X8i against X2i and calls out improvements in performance, memory capacity, and bandwidth. citeturn0search0turn0search1turn1search2

To understand what this upgrade means on the ground, it helps to remember what the recent memory-optimized landscape looked like:

  • X2idn is a memory-optimized Intel-based family powered by 3rd Gen Intel Xeon Scalable and can scale up to 2,048 GiB RAM, with up to 100 Gbps network and 80 Gbps EBS on certain sizes. citeturn1search0
  • X8i pushes the ceiling to 6,144 GiB (6TB class) and focuses heavily on memory bandwidth, while also offering up to 100 Gbps network and 80 Gbps EBS in the largest sizes. citeturn0search1turn1search2

The interesting part isn’t that AWS can sell you more RAM. AWS has been in the “multi-terabyte RAM club” for a while across different families. The interesting part is AWS indicating that the memory subsystem improvements are large enough to advertise a multi‑X increase—because that’s the difference between “it’s bigger” and “it’s faster in the ways your workload feels.” citeturn0search1turn1search2

Workload Fit: Who Should Care About X8i?

Let’s translate AWS’s official positioning into the sort of use-case decision you’d make on a Monday morning after coffee (or, for some of us, after the second coffee).

1) SAP HANA and SAP-certified enterprise stacks

AWS positions X8i as SAP-certified and suitable for in-memory databases such as SAP HANA. citeturn0search0turn0search1turn1search2

In the SAP world, certification is more than a checkbox; it is often a gating factor for production deployments, support agreements, and how comfortable auditors feel when they show up uninvited. AWS has a long history of announcing SAP-certified instance families (it has been doing this for many generations), often tying certification to SAP platform notes and sizing metrics like SAPS. citeturn2search3turn2search4turn2search5

AWS claims up to 50% higher SAPS performance for X8i versus X2i. citeturn0search0turn0search1turn1search2

Why this matters: SAP HANA is famously sensitive to memory throughput and latency—because the “HANA” in your architecture diagram is quite literally the part that wants to keep huge working sets in RAM and operate on them at speed. When your database fits in memory but can’t be scanned quickly, the user experience still feels like a spinning wheel.

2) Large databases: PostgreSQL, SQL Server, Oracle-style workloads

AWS calls out “traditional large-scale databases” and includes a performance claim of up to 47% faster PostgreSQL performance versus X2i. citeturn0search0turn0search1turn1search2

Even for “traditional” databases, a lot of modern performance work is about keeping hot indexes, buffers, and cached results resident in memory, while also pushing high throughput to storage for redo/undo logs, checkpoints, and backups.

That’s where the combination of:

  • very large RAM ceilings (up to 6TB), citeturn0search1turn1search2
  • high network throughput (up to 100 Gbps), citeturn0search1turn1search4
  • and high EBS throughput (up to 80 Gbps) citeturn0search1turn1search4

…becomes practical, not just theoretical.

3) Caching layers: Memcached (and the “it’s just cache” fallacy)

AWS claims X8i delivers up to 88% faster Memcached performance compared to X2i. citeturn0search0turn0search1turn1search2

Cache systems are often treated as simple, but at scale they can be surprisingly demanding: large in-memory datasets, high request rates, and nasty tail-latency behavior when you run into CPU contention or memory subsystem limits. If your cache is “slow,” your whole architecture becomes “slow,” because you’ve built the entire app on the assumption that cache is fast.

In other words: faster Memcached isn’t just about Memcached. It can mean fewer cache nodes, less cross-AZ chatter, and fewer cascading cache misses that dump read spikes onto your database.

4) Electronic Design Automation (EDA)

AWS explicitly lists EDA as a target workload class for X8i. citeturn0search0turn0search1turn1search2

EDA workloads are typically a brutal mix of:

  • large memory footprints
  • high parallelism (but not always perfect scaling)
  • long job durations where performance-per-dollar is existential

In this space, single-node scale-up still matters. Some steps in chip design flows (timing analysis, place-and-route, verification) can benefit from huge memory and strong single-system throughput even when you also have distributed compute options.

5) “Wait, why is AI inference in a memory-optimized instance announcement?”

AWS also claims up to 46% faster AI inference performance compared with X2i. citeturn0search0turn0search1turn1search2

This is an important reminder that not all inference is GPU inference. Many production inference workloads are:

  • CPU-based (especially for smaller models, classical ML, or cost-sensitive workloads)
  • memory-resident (models, embeddings, and feature stores can be large)
  • latency-sensitive (p99 matters more than peak throughput)

Faster memory bandwidth can help when inference is bottlenecked by feeding the CPU efficiently—particularly in recommender systems and embedding-heavy flows where memory access patterns can dominate.

Customer Examples From the Announcement: What They Suggest

AWS’s blog post includes a couple of preview-phase customer stories that hint at why this family exists.

RISE with SAP: using the full 6TB RAM ceiling

AWS notes that during the preview, RISE with SAP used up to 6 TB of memory capacity and saw 50% higher compute performance compared to X2i instances for SAP HANA workloads, enabling faster transaction processing and improved query response times. citeturn0search1

The key takeaway here isn’t the brand name; it’s that AWS is seeing enough demand for multi-terabyte HANA footprints that it’s worth productizing a new family with a 6TB ceiling and higher bandwidth. That’s the scale where “just add another node” isn’t always the right answer, either because of architecture, licensing, operational complexity, or performance characteristics.

Orion: fewer active cores to cut SQL Server licensing costs

AWS also says Orion reduced the number of active cores on X8i instances (compared to X2idn instances) while maintaining performance thresholds, cutting SQL Server licensing costs by 50%. citeturn0search1

This story lands because it points at an underappreciated reality: in enterprise computing, you’re not only paying AWS. You’re often paying your database vendor more than you pay AWS—especially if the software is licensed per core.

Microsoft’s licensing guidance describes core-based licensing, including minimums per processor and the principle that licensing depends on cores assigned/running depending on model. citeturn2search0turn2search1

AWS itself has published prescriptive guidance on optimizing costs for SQL Server workloads, including how reducing active vCPUs (where applicable) can reduce licensing requirements and costs. citeturn2search2

Translation: If X8i’s higher per-core performance and memory throughput lets you meet SLAs with fewer cores, it can change your true total cost—sometimes dramatically—even if the instance hourly rate is higher than an older generation.

Under the Hood: Nitro, Bare Metal, and Why AWS Keeps Talking About Cards

X8i is built on the AWS Nitro System and uses sixth-generation AWS Nitro cards to offload virtualization, storage, and networking functions. citeturn0search1turn1search2turn2search6

If you haven’t followed Nitro closely, the short version is: AWS moved a lot of the “cloud plumbing” out of the main CPU path and into dedicated components, which can improve performance consistency and reduce overhead, while also strengthening security boundaries.

AWS describes Nitro as offloading functions to dedicated hardware and software, aiming to deliver “practically all of the resources of a server to your instances,” and emphasizes security properties like a minimized attack surface and a model that eliminates administrative access to customer instances. citeturn2search6

AWS’s Nitro security design documentation also discusses how keys for encryption (EBS, instance storage, and VPC networking) are held within Nitro hardware boundaries in protected memory, inaccessible to AWS operators and customer code on the host processors. citeturn2search7

In a family like X8i—where customers may run massive, business-critical databases—those security assurances and performance isolation properties are part of the product, not marketing garnish.

Why bare metal still exists in 2026

X8i includes bare metal options (metal-48xl and metal-96xl). citeturn0search1turn1search2

Bare metal can matter when you have:

  • specialized licensing models
  • kernel-level performance instrumentation needs
  • custom hypervisors or security tooling
  • workloads that are extremely sensitive to virtualization nuance

And sometimes it matters because an enterprise procurement process somewhere still has a form that says “physical server,” and it doesn’t care that it lives in a hyperscale data center. I don’t make the rules; I just report on their existence.

Instance Bandwidth Configuration (IBC): A Small Feature With Big Consequences

X8i supports Instance Bandwidth Configuration (IBC), which lets you shift bandwidth allocation between network (VPC) and EBS by up to 25%. citeturn0search1turn1search2turn1search3

AWS describes IBC as a way to adjust VPC and EBS service bandwidth by up to 25%, with the tradeoff that increasing one reduces the other; it doesn’t increase burst bandwidth, packets per second, or IOPS. citeturn1search3

Why you should care: Many database deployments have phases where storage bandwidth matters more (checkpoints, backups, log flushes) and phases where network matters more (replication, sharding, feeding analytics jobs). A knob that lets you bias bandwidth one way or the other—without changing instance type—can be surprisingly valuable for tuning.

Regional Availability: Where You Can Run X8i Right Now

As of the GA announcement (January 15, 2026), AWS lists X8i availability in:

  • US East (N. Virginia)
  • US East (Ohio)
  • US West (Oregon)
  • Europe (Frankfurt)

These regions are explicitly called out in both the AWS What’s New item and the AWS News Blog post. citeturn0search0turn0search1

If you’re outside those regions, the practical question is whether you can move the workload (data gravity says “no,” your CFO says “maybe,” your latency budget says “stop asking”). For production databases, region availability is often the deciding factor, not raw spec attractiveness.

Pricing and Purchasing Models: The Usual AWS Menu

AWS says X8i instances can be purchased as On-Demand, via Savings Plans, and as Spot Instances. citeturn0search0turn0search1

Spot is a fun option for the right workloads (stateless, fault tolerant, batchy). For memory-intensive databases, it’s usually “fun” the way replacing a RAID controller at 2 AM is fun. But for analytics or EDA jobs, Spot can be a legitimate cost lever—especially if you architect checkpointing correctly.

X8i vs “Just Use a Bigger Cluster”: Scale-Up Is Not Dead

Cloud architecture advice over the last decade often boils down to “scale out.” And yes, horizontally scalable systems are great when you can actually scale horizontally without breaking correctness, performance, or your sanity.

But there are still many cases where vertical scaling (a bigger single node) is the right move:

  • In-memory databases where partitioning increases cross-node coordination
  • Enterprise workloads where licensing and support models are tied to specific deployment patterns
  • Latency-sensitive systems where network hops create p99 pain
  • Operations simplicity: fewer nodes, fewer failure modes, fewer “why is this shard hot” mysteries

By offering up to 6TB RAM and pushing memory bandwidth hard, AWS is implicitly acknowledging that the world still runs a lot of “big iron” problems—just with cloud APIs instead of forklift deliveries.

Industry Context: Intel’s Cloud Positioning and the “Custom CPU” Era

There’s also a vendor dynamics story here. Hyperscalers increasingly build custom silicon (AWS Graviton, Trainium, Inferentia) and also buy semi-custom variants from traditional CPU vendors. X8i is part of that “semi-custom” spectrum: it’s still Intel x86, but tuned for AWS’s platform.

Industry coverage around Intel’s data center roadmap frequently returns to memory throughput and platform capabilities as key competitive battlegrounds. For example, Tom’s Hardware has reported on Intel roadmap decisions emphasizing higher memory channel counts and memory bandwidth as a core differentiator for future server parts. citeturn0news14

Even without diving into every socket and DIMM detail (which AWS doesn’t publish for its custom parts anyway), it’s clear that memory throughput is becoming a primary metric again—especially for data-heavy, inference-heavy, and database-heavy workloads.

Practical Guidance: When to Choose X8i (and When Not To)

Choose X8i when…

  • You run SAP HANA or other SAP-certified workloads and need high memory capacity and throughput. citeturn0search0turn0search1
  • Your database performance is memory bandwidth bound and you’ve already tuned indexes, caching, and query plans.
  • You can benefit from scale-up simplicity (fewer nodes, less replication complexity).
  • You need high EBS throughput and network bandwidth on a single host for heavy logging, backups, or replication. citeturn0search1turn1search4

Think twice when…

  • Your workload is primarily compute-bound and fits comfortably in smaller memory footprints (you might get better economics on other families).
  • You depend on local NVMe instance storage semantics from other memory-optimized families (X8i is listed as EBS-only on its instance type page). citeturn1search2
  • You need the instance type in regions beyond the current GA list (unless you can wait). citeturn0search0turn0search1

The Bottom Line

EC2 X8i is AWS doubling down on a simple message: for modern enterprise and data workloads, memory bandwidth is a first-class performance feature again—not an afterthought behind core counts.

With up to 6TB RAM, up to 3.4x more memory bandwidth than X2i, up to 100 Gbps networking, and up to 80 Gbps EBS throughput, X8i targets the kinds of workloads that still pay the bills for many large companies: SAP, big databases, and analytics systems that can’t simply be “microserviced” into submission. citeturn0search1turn1search2turn1search4

And yes, it also quietly acknowledges a reality of 2026: you can optimize cloud spend not just by right-sizing instance hourly costs, but by reducing external licensing costs when better per-core performance lets you use fewer cores. In enterprise land, that’s not a footnote—it’s often the plot.

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org