Your bottleneck isn’t CPU. It’s your scratch disk.
You’ve felt it before: fast CPUs, big memory, then everything crawls during shuffles, spills, staging. That’s the classic EBS tax for jobs that live and die on local I/O.
Here’s the switch flip: AWS just launched Amazon EC2 C8id, M8id, and R8id with up to 22.8 TB of local NVMe SSD attached to the host. Translation: less time watching spinners, more time shipping. They run custom Intel Xeon 6 processors tuned for AWS, with DDR5 and AVX-512, and tie into Nitro with up to 100 Gbps networking and EFA for low-latency HPC moves.
If your jobs need fast temporary space—Spark shuffles, in-memory databases with spill, video encoding, scientific scratch, CPU inference—this is your new favorite button. You’re not just getting more storage. You’re getting 3x vCPUs, memory, and local storage versus prior-gen families, and R8id specifically posts a 43% performance jump over R6id.
Use the instance store right, and you can squeeze >7 GB/s per drive in RAID setups, crush tail latencies, and stop overpaying for IOPS you don’t use.
When your workload spills or shuffles, EBS (even io2 Block Express) can choke. With instance store NVMe physically on the host, you trade network hops for PCIe lanes. Net result: huge sequential throughput and microsecond access that speeds your “throwaway” data paths.
AWS’ launch pitch is clear: “up to 22.8 TB of local NVMe SSD storage” on C8id, M8id, and R8id. In practice, RAID 0 across multiple NVMe devices gives linear throughput gains, often pushing beyond 7 GB/s per drive for sequential reads and writes. If your Spark jobs spend half their time on shuffle I/O, that lever moves your wall-clock.
Think of it this way: moving shuffle, temp, and cache reads from network storage to on-host NVMe cuts a round trip. EBS is great, but it’s still a network hop with per-volume throughput and IOPS limits. Local NVMe gives microseconds not milliseconds, and wide lanes you don’t reserve ahead of time. For big sequential spills (ETL, sorting, encoding) and high-entropy shuffles, that’s the gap between CPUs idling and CPUs sprinting.
To be clear, io2 Block Express can deliver serious performance—hundreds of thousands of IOPS and multi-GB/s per volume when provisioned right. But if your pattern is “generate, chew, discard,” the provisioning tax and network hop often become the bottleneck under concurrency. Instance store flips that script.
Pro tip: don’t judge by a single-thread test. NVMe shines under concurrency. Use a multi-threaded workload (or a tool like fio with parallel jobs and queue depth) to measure the real curve your app will see.
EBS stays gold for durable state, database volumes that need snapshots, and workloads that like decoupled storage and compute scaling. Instance store is ephemeral. If the instance stops or fails, data is gone. The winning pattern: keep hot scratch on NVMe and persist data you care about to EBS or S3.
As AWS docs put it, “An instance store is temporary block-level storage for your instance.” Use it deliberately, and you get both speed and safety.
"An instance store is temporary block-level storage for your instance." — AWS EC2 Documentation
Use this split-brain approach:
Two common patterns:
C8id extends the compute-optimized C8i family and adds local NVMe. You still get the tight 2:1 memory-to-vCPU profile for CPU-heavy tasks—web proxies, encoding, CPU inference—and now you feed them fast scratch. AWS benchmarks show 60% faster NGINX, 40% faster AI recommendation inference (e.g., TensorFlow Serving), and 35% faster Memcached vs. prior gen C7id. The c8id.96xlarge tops out at 384 vCPUs, 768 GiB memory, and 22.8 TB NVMe.
If you’ve been running inference with batch pre and post-processing that spills to disk, this is a clean win. Keep transient tensors, feature caches, and logs on NVMe to shave tail latency.
Try this mapping to get started fast:
M8id balances compute and memory while layering in big local storage. Think mid-tier databases with temp tables, ERP workloads that burst during ETL windows, CI or CD pipelines that fan out builds and cache artifacts locally. Sample sizes scale from m8id.large with 0.95 TB NVMe up to m8id.96xlarge with 22.8 TB NVMe and 3,072 GiB of RAM. It’s the versatile choice when your architecture has many moving parts but you don’t want to overfit a specialized family.
The twist: you can keep durable data on EBS, but move temp tables, package caches, and build artifacts to NVMe for immediate gains.
Real-world examples that map well to M8id:
R8id is memory-optimized plus local NVMe. It’s built for in-memory databases (SAP HANA, Redis), real-time analytics (Spark), big caches, simulation workloads, and EDA. AWS reports up to 43% higher performance than R6id, 3.3x higher memory bandwidth, and triple the local storage—topping at 22.8 TB on r8id.96xlarge with 3 TB of DDR5.
If your HANA box spills during delta merges or your Redis snapshot pipeline thrashes, R8id shrinks the penalty by keeping temporary I/O on-device. Pair with EFA for tightly coupled HPC patterns when you need low-latency east-west traffic.
"R8id provides up to 43% higher performance than R6id." — AWS launch materials
Best practices for memory-first stacks:
Instance store is ephemeral by design. Great for scratch, terrible for irreplaceable state. The playbook:
AWS says it plainly in docs: instance store is temporary block-level storage. That’s not a bug; it’s a feature when you’re optimizing throughput with acceptable risk boundaries.
Operational add-ons that save headaches:
Don’t forget placement groups for full-bisection bandwidth across nodes. And when you’re mixing EBS and instance store, ensure your EBS bandwidth (up to 80 Gbps on the largest sizes) isn’t your new bottleneck.
"Use RAID 0 on instance store to increase volume size and to provide higher I/O performance." — AWS EC2 User Guide (RAID)
A few more low-effort wins:
If you’re throwing EBS io2 Block Express at scratch problems, you might be paying a premium for durability and provisioned IOPS you don’t need. Local NVMe gives you raw, attached performance without per-IOPS pricing. For shuffle-heavy analytics or build farms, that really matters.
A simple framing for finance: if your job runtime drops 30–50% by moving scratch to instance store, your compute-hours fall accordingly. Combine that with up to 15% better price-performance on C8id versus C7id and the savings stack fast. Add Savings Plans for baseline capacity and Spot Instances for burst windows.
Two extra levers to remember:
You didn’t just buy performance. You bought back time. And time is the only non-renewable resource in your roadmap.
"Savings Plans provide flexible pricing for EC2 usage in exchange for a 1- or 3-year term." — AWS Savings Plans
If you want to push savings further:
These families snap into AWS Nitro for security isolation, support IMDSv2, and can leverage Nitro Enclaves for confidential compute when you need to segment sensitive workloads on the same host.
Add in the operational niceties:
As the Xeon 6 lineup matures, expect tighter perf-per-watt curves and broader region availability. In parallel, Graviton remains the ARM path for specific price-performance wins—but in x86-heavy stacks (legacy software, proprietary binaries, AVX-accelerated libraries), C8id, M8id, and R8id are the straightforward upgrade. The big story isn’t CPU versus CPU; it’s end-to-end throughput from core to cache to disk to network.
"Elastic Fabric Adapter provides low-latency, high-throughput communications for tightly coupled HPC applications." — AWS Documentation
If you’re modernizing, consider a two-lane approach:
Bonus guardrails that pay off:
No. Instance store volumes are ephemeral. If the instance stops, fails, or is terminated, data on instance store is lost. Keep irreplaceable data on EBS or S3 and use instance store for caches, shuffles, and other scratch paths.
AWS indicates sequential reads and writes can exceed 7 GB/s per drive. With multiple devices in RAID 0, throughput scales nearly linearly for large sequential workloads. Your actual performance depends on filesystem, queue depth, and workload profile.
The largest c8id.96xlarge, m8id.96xlarge, and r8id.96xlarge configurations offer up to 22.8 TB of NVMe instance storage, alongside up to 384 vCPUs. R8id also reaches 3 TB of DDR5 memory on its largest sizes.
When you need durable, high-IOPS storage with snapshots, encryption at rest, and decoupled scaling from compute. Databases that require persistence across reboots should keep primary volumes on EBS, while offloading temp and spill files to instance store.
Yes. They integrate with Nitro and support ENA up to 100 Gbps networking on larger sizes. Elastic Fabric Adapter is available for HPC-style, low-latency communications. Always confirm instance-size-specific limits in the latest AWS documentation.
Use Savings Plans for steady-state capacity and EC2 Spot for bursty or fault-tolerant jobs. Right-size instances, move scratch to NVMe to reduce runtime, and monitor utilization. The combination can deliver up to 15% better price-performance versus prior gens, plus time savings.
Not directly. Instance store doesn’t support snapshots like EBS. If you need to preserve data, copy it to EBS or S3 on a schedule or at job boundaries.
On Nitro-based instances, instance store data is encrypted at rest and is securely wiped when the instance is stopped or terminated. You still control application-layer encryption for data written to EBS or S3.
Yes. Point your container runtime’s image and cache directories to the NVMe mount. You’ll see faster pulls and layer extraction, especially in CI or CD pipelines.
You don’t need more servers—you need more throughput where it counts. These new EC2 families put fat NVMe lanes right next to your CPUs, so your hot code paths stop tripping over storage. Move scratch, spills, and temp artifacts onto instance store, keep your state safe on EBS or S3, and let the CPUs breathe. Your jobs get shorter, your bills get saner, and your team gets weekends back.
In 2026, the fastest “upgrade” isn’t more cores—it’s fewer I/O hops.