Amazon EC2 X8i Is Here: AWS’s 6 TB Memory Monsters, Custom Xeon 6 Silicon, and What It Means for SAP HANA and Big Databases

AWS just did what AWS does best: quietly turn a very specific enterprise pain point into a new instance family, then casually drop performance numbers that make your current fleet feel like it’s running on politely overclocked calculators.

On January 15, 2026, Amazon Web Services announced the general availability of Amazon EC2 X8i, a new memory-optimized instance family powered by custom Intel Xeon 6 processors that are available only on AWS. The headline features are hard to miss: up to 6 TB of memory, up to 3.4x more memory bandwidth than the prior generation (X2i), up to 43% higher performance overall, and scaling up to 384 vCPUs in the top sizes. AWS’s “What’s New” post is the official GA stamp. citeturn1search1

The longer story is more interesting than the spec sheet: X8i is AWS signaling that “memory-heavy, CPU-sensitive, licensed-to-death enterprise workloads” are still a big enough market to justify custom CPU bins, specialized memory configurations, and new instance sizing that targets the awkward reality of SAP HANA, large OLTP/OLAP databases, in-memory caches, EDA, and anything else that tends to turn RAM into a black hole.

This article is based on the original AWS News Blog item, “Amazon EC2 X8i instances powered by custom Intel Xeon 6 processors are generally available for memory-intensive workloads”, written by Channy Yun. citeturn1search3

What AWS actually launched: the EC2 X8i family in plain English

X8i is part of AWS’s memory-optimized lineup (the “X” family). The pitch is not subtle: if your workload is constrained by memory capacity and memory bandwidth (not just raw CPU), this is the machine you want.

AWS is positioning X8i for:

In-memory databases (especially SAP HANA)
Large-scale traditional databases (SQL Server, PostgreSQL, and friends)
Data analytics workloads with large working sets
Electronic Design Automation (EDA) (a.k.a. “how to make silicon using mountains of RAM”)

That’s consistent across AWS’s GA announcement and the X8i product page. citeturn1search1turn1search0

14 sizes, including bare metal, up to 6 TB RAM

X8i ships in 14 sizes, including two bare-metal variants. AWS’s News Blog post includes a full table of vCPU and memory sizes. The lineup starts at x8i.large (2 vCPUs, 32 GiB) and tops out at x8i.96xlarge (384 vCPUs, 6,144 GiB). citeturn1search3

At the high end, the key point isn’t “wow, 384 vCPUs” (we’ve seen big boxes in cloud for years). The point is that AWS is aiming squarely at systems that normally live on expensive, multi-terabyte RAM servers in carefully climate-controlled rooms with a “please don’t reboot” vibe.

Networking and EBS bandwidth: it’s built to move data, not just store it in RAM

AWS says X8i can deliver up to 100 Gbps of network bandwidth and up to 80 Gbps of throughput to Amazon EBS at the top sizes, and supports Elastic Fabric Adapter (EFA). citeturn1search3

That matters because memory-intensive workloads rarely live in isolation. SAP systems talk to application servers, caches, integration layers, and backup/replication systems. Analytics pipelines read from S3, write to EBS, and sling data across the VPC. If you “only” upgrade compute, but your storage logging or replication is throttled, you just moved the bottleneck, not eliminated it.

The silicon angle: “custom Intel Xeon 6 processors” and why you should care

AWS says X8i is powered by custom Intel Xeon 6 processors with a sustained all-core turbo frequency of 3.9 GHz, and that these processors are available only on AWS. citeturn1search3turn1search0

When a hyperscaler says “custom CPU,” it can mean several things:

Different core counts or cache configurations
Different memory support and tuning
Different turbo behavior and power envelopes
Different platform validation and firmware stacks

AWS doesn’t publish the full microarchitectural configuration (and it typically doesn’t). But AWS does emphasize sustained all-core turbo and memory bandwidth, which is the right emphasis for this category.

Memory bandwidth is the quiet kingmaker for in-memory systems

Many enterprise buyers still think in terms of “how many cores?” and “how much RAM?” But in-memory databases and giant caches can become memory bandwidth bound: the CPU is ready to compute, but it’s waiting on data movement.

Intel’s own Xeon 6 positioning leans heavily into memory throughput improvements via DDR5 and MRDIMMs (Multiplexed Rank DIMMs), noting that MRDIMMs can provide more than 37% greater memory bandwidth than RDIMMs and can reach up to 8,800 MT/s in expected data transfer rate. citeturn0search4

AWS specifically mentions X8i uses DDR5 7200 MT/s DIMMs to deliver up to 3.4x more memory throughput than X2i. citeturn1search0

Translation: AWS is selling not just “more RAM,” but faster RAM in a platform designed to keep that memory fed under sustained load.

X8i vs X2i: the generational upgrade story (and why it’s not just marketing)

AWS draws a direct comparison to the previous-generation X2i family:

1.5x more memory capacity (up to 6 TB)
Up to 3.4x more memory bandwidth
Up to 43% higher performance overall

Those aren’t tiny improvements; they’re the kind of step change that can alter architecture decisions (scale-up vs scale-out), and cost decisions (license per core, memory per node, replication topology, etc.). citeturn1search3turn1search1

AWS also lists workload-specific improvements compared to X2i:

Up to 50% higher SAPS performance
Up to 47% faster PostgreSQL
Up to 88% faster Memcached
Up to 46% faster AI inference

As always, “up to” is doing some heavy lifting. But the variety of benchmarks (ERP-style SAPS, database performance, caching, inference) suggests improvements aren’t confined to one niche microbenchmark. citeturn1search1turn1search3

Why the memory bandwidth claim matters more than the performance claim

Performance claims are slippery because they depend on workload, configuration, and tuning. Memory bandwidth improvements, however, tend to be structural. If the platform genuinely delivers materially higher bandwidth, then a broad range of “big working set” workloads can benefit—even if your specific application doesn’t hit the AWS headline number.

For the kinds of customers buying multi-terabyte memory instances, the bottleneck is often not a single query. It’s the entire system’s ability to sustain throughput while doing “everything at once”: OLTP transactions, analytics queries, replication, backups, and the inevitable “someone ran a report at 9 AM” incident.

The SAP HANA angle: AWS is targeting the “this cannot be slow” part of enterprise

AWS repeatedly highlights that X8i is SAP certified (and the X8i page calls out SAP HANA certification as part of the positioning). citeturn1search3turn1search0

This is not accidental. SAP HANA remains one of the most demanding mainstream enterprise workloads in terms of memory, sustained CPU performance, and storage logging behavior. It’s also one of the most expensive, which means even small efficiency improvements can translate into very large invoices getting slightly less terrifying.

RISE with SAP: why AWS is leaning into managed SAP journeys

In the AWS News Blog GA post, AWS notes that during preview, customers such as RISE with SAP used up to 6 TB of memory capacity and saw 50% higher compute performance compared to X2i for SAP HANA, improving transaction processing and query response time. citeturn0search0

That matters because RISE is SAP’s flagship “we’ll help you modernize (and move) your ERP” program, and hyperscalers want to be the preferred landing zone for those migrations. If AWS can offer bigger, faster memory instances with strong SAP certifications, it reduces friction for customers who are trying to consolidate landscapes, reduce operational risk, or hit performance SLAs during migration windows.

Licensing economics: the unspoken driver behind “faster per core”

AWS also shares a preview anecdote: a customer called Orion reportedly reduced the number of active cores on X8i compared to X2idn while maintaining performance thresholds, cutting SQL Server licensing costs by 50%. citeturn0search0

If you’ve ever tried to optimize SQL Server or Oracle licensing in the cloud, you know that “CPU performance per licensed core” is a bigger deal than most cloud marketing teams will ever admit in public. If X8i’s sustained turbo and memory performance allow consolidation into fewer licensed cores, that can outweigh raw infrastructure cost differences.

Instance Bandwidth Configuration (IBC): a small feature with big practical impact

X8i supports Instance Bandwidth Configuration (IBC), which lets you adjust how the instance allocates bandwidth between VPC networking and Amazon EBS. AWS says this can shift bandwidth by up to 25% to better suit your workload (for example, more EBS bandwidth for database logging, or more VPC bandwidth for replication or data movement). citeturn1search3turn1search4

AWS’s documentation explains that using an EBS-focused weighting (for example ebs-1) increases baseline EBS bandwidth by 25% while reducing VPC bandwidth by the same absolute amount. It also clarifies what IBC doesn’t do: it doesn’t increase burst bandwidth, packets per second, or IOPS. citeturn1search4turn1search2

In practical terms, IBC is a knob you can turn when you’re tuning systems that aren’t neatly categorized as “network-heavy” or “storage-heavy.” Many real-world database systems are both, depending on time of day and operational events (ETL windows, backups, replication catch-up, etc.).

A quick scenario: when you’d actually use IBC

Imagine a large PostgreSQL or SQL Server deployment:

During peak business hours, you’re heavy on client connections and replication traffic, so VPC bandwidth matters.
During batch windows, you’re heavy on write-ahead logging, bulk loads, and snapshot/backup operations, so EBS throughput matters.

IBC gives you the ability to pick a bias that matches your “most painful” bottleneck—without changing instance type. That’s not glamorous, but it’s exactly the kind of feature that keeps production engineers sane.

Where X8i is available (as of January 2026)

At GA, AWS says X8i is available in:

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

This regional footprint is typical for brand-new, high-end instance families: start with a few major regions with the right capacity and demand profile, then expand. citeturn1search1turn1search3

Who should care about X8i (and who really shouldn’t)

Let’s save everyone time: not every workload needs 6 TB of RAM. Most don’t even know what they’d do with it besides run a truly magnificent in-memory cache of… something.

You should care if you run any of these

SAP HANA (directly, or via a managed SAP program)
SQL Server or PostgreSQL with very large buffer pools / working sets
Memcached fleets where hit rate is revenue and latency is reputation
Analytics systems that repeatedly scan or join huge in-memory datasets
EDA workloads where “memory hungry” is an understatement, not a label

AWS explicitly calls out SAP HANA, large databases, analytics, and EDA in its GA messaging. citeturn1search1turn1search3

You probably shouldn’t care (yet) if you’re doing this instead

General web apps with moderate caching needs (use R or M families)
Horizontal scale-out analytics (Spark clusters that scale by adding nodes)
GPU-heavy ML training (your problem is not “more DDR5,” it’s “more CUDA”)
Startups that think “in-memory database” means “we set Redis to maxmemory 2GB”

X8i is a scalpel. If you need a hammer, you’ll pay scalpel prices to hit nails.

How X8i fits into AWS’s broader compute strategy

It’s tempting to look at X8i as just another instance type. But the deeper trend is that AWS (like other hyperscalers) is building a compute catalog that resembles a full hardware vendor lineup—only you rent it by the hour.

Over the past few years, AWS has:

Expanded Graviton (ARM) broadly for cost/performance
Continued to ship new Intel generations for enterprise compatibility and memory-heavy niches
Kept AMD instance families relevant in general-purpose and compute-optimized profiles
Invested heavily in Nitro to reduce virtualization overhead and improve security isolation

X8i sits in the “premium enterprise performance” lane, where compatibility, certifications, and predictable performance tend to beat out “cheapest per vCPU” metrics.

Nitro and the “custom CPU + custom platform” pattern

AWS notes X8i uses sixth-generation AWS Nitro cards to offload virtualization, storage, and networking functions, improving performance and security. citeturn0search0

This is the part where AWS wins quietly. A modern EC2 instance isn’t just a CPU and RAM; it’s an integrated platform where network, storage, and virtualization behavior can decide whether your latency is stable or whether it looks like a heart monitor in a medical drama.

What the performance claims likely mean in the real world

AWS’s GA post highlights several “up to” figures. Let’s interpret them in a way that won’t get you yelled at in a capacity planning meeting.

SAPS performance: meaningful for SAP sizing, not just bragging rights

AWS says X8i delivers up to 50% higher SAP Application Performance Standard (SAPS) performance compared to X2i. citeturn1search3

For SAP customers, SAPS isn’t just a benchmark; it’s often part of sizing conversations and procurement decisions. A higher SAPS result can translate into either:

More headroom at the same instance size
Similar headroom at a smaller size (and potentially fewer licensed cores)

Either outcome is attractive if you’re trying to de-risk a migration or consolidate landscapes.

PostgreSQL and Memcached: a reminder that “memory optimized” isn’t only for SAP

AWS cites up to 47% faster PostgreSQL and up to 88% faster Memcached performance versus X2i. citeturn1search1

Even if you’re not running SAP, this is interesting because it suggests X8i can improve both:

Traditional database workloads (query execution, buffer pool behavior, background maintenance)
High-throughput in-memory caching (where memory bandwidth and CPU per-core speed can matter a lot)

Memcached in particular can be surprisingly sensitive to CPU frequency, memory subsystem performance, and network characteristics. If your cache tier is a bottleneck, the fastest fix is often “make the cache tier better” rather than rewriting the application to cache less (which is what people say right before they do nothing for six months).

AI inference performance on CPU: the “not everything needs a GPU” argument

AWS also mentions up to 46% faster AI inference compared to X2i. citeturn1search1

This is aligned with Intel’s Xeon 6 positioning around Intel AMX (Advanced Matrix Extensions), which accelerates common inference data types (INT8, BF16) and supports FP16-trained models. citeturn0search4

CPU inference isn’t glamorous, but it’s real. Many organizations run smaller or medium-sized models, or run inference as part of a broader CPU-bound data pipeline. If you can keep inference on CPU without sacrificing latency or throughput, you simplify operations and avoid GPU capacity constraints. X8i isn’t an “AI instance family,” but its CPU capabilities and memory bandwidth can be beneficial for certain inference-heavy services.

Cost and purchasing models: On-Demand, Savings Plans, and Spot

AWS says X8i is purchasable via On-Demand, Savings Plans, and Spot. citeturn1search1turn1search3

For the large memory sizes, most production customers will realistically end up in one of these patterns:

On-Demand for migration windows, DR tests, short-term capacity spikes, and proof-of-concepts
Savings Plans for steady-state production (because nothing says “we’re committed” like a 1- or 3-year discount agreement)
Spot for non-critical analytics, batch, and EDA-style workloads that can handle interruptions (and for the brave)

AWS doesn’t publish pricing in the GA post itself; you’ll need to consult EC2 pricing for exact numbers. citeturn1search3

Practical migration guidance: how to evaluate X8i without getting hurt

If you’re already on X2i (or another memory-optimized family), the evaluation path is fairly straightforward—but the stakes are high because these are big systems and big bills.

1) Start with a workload profile, not an instance type

Before you spin up anything, answer these:

Are you memory capacity constrained, memory bandwidth constrained, or CPU constrained?
Is your pain point steady-state throughput or tail latency during concurrency spikes?
Are you paying per-core licenses (SQL Server/Oracle/SAP components) that could change the economics?

2) Benchmark with your real operational profile

Use a test that mimics:

Peak business transaction volume
Reporting queries running concurrently
Backups and log shipping / replication events

Memory-optimized instances shine (or disappoint) under concurrency. A single-user benchmark rarely tells the full story.

3) Test IBC if you have mixed network/EBS contention

If your workload includes heavy EBS logging and heavy replication, test IBC settings. AWS documentation clarifies the tradeoff: you can boost baseline EBS bandwidth by 25% at the cost of the same absolute amount of VPC bandwidth (or vice versa). citeturn1search4

4) Don’t ignore operational constraints

At this tier, operational considerations can matter as much as raw performance:

Regional availability (your preferred region might not have X8i yet) citeturn1search1
Host placement and capacity planning for the largest sizes
Disaster recovery strategy (multi-region, multi-AZ, replication bandwidth)

Competitive context: Intel in the cloud, and why AWS still ships it

Cloud compute in 2026 is a three-way conversation: AWS’s ARM-based Graviton for cost/performance, AMD EPYC for strong general-purpose performance in many providers, and Intel Xeon continuing to dominate a lot of enterprise-standardization and compatibility narratives.

Third-party coverage has highlighted that custom Xeon 6 wins matter for Intel amid intense competition, and has connected the same custom Xeon 6 platform approach to other AWS instance families like R8i. citeturn1news12

For AWS, the strategy is pragmatic: deliver the best tool for the job, especially when the job is “run the world’s most expensive ERP system without making the CFO cry.” Intel’s ecosystem, certifications, and platform maturity remain valuable in that context.

Security and isolation: why Nitro is part of the value proposition

AWS’s GA post reiterates that X8i uses the AWS Nitro System and Nitro cards that offload virtualization, storage, and networking. The key point here is that in modern EC2, performance and security isolation are tightly coupled with platform design. citeturn0search0

For large enterprise workloads, especially regulated ones, predictability matters: you want consistent performance characteristics, well-understood virtualization boundaries, and platform features that reduce “noisy neighbor” risk. Nitro’s offload architecture is AWS’s long-running answer to that.

So, should you move to X8i now?

Here’s the pragmatic take:

If you’re on X2i and you are memory bandwidth constrained, X8i is likely worth a serious evaluation based on AWS’s stated 3.4x bandwidth uplift and workload benchmark claims. citeturn1search3turn1search1
If you are licensing-limited (SQL Server, etc.), X8i’s potential for higher per-core performance could change your licensing math—sometimes dramatically, as AWS’s Orion example suggests. citeturn0search0
If you’re not already in the “multi-terabyte RAM” club, you probably don’t need X8i, and you’ll get better cost/performance elsewhere.

And as with any new instance family: run a proof-of-concept, validate stability, watch your NUMA and memory locality behavior, and don’t assume “up to” equals “for me.” AWS is giving you a faster engine; you still need to tune the car.

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org