storagehardwaredevops

PLC Flash Memory: What Developers Need to Know About the New SK Hynix Cell-Splitting Approach

ooracles

2026-01-21

11 min read

SK Hynix’s cell‑splitting PLC changes SSD tradeoffs. Learn what it means for storage tiers, wear‑leveling, and lifecycle management in 2026.

Hook: Why PLC Flash Cell-Splitting Matters to Developers and Sysadmins in 2026

Data teams, platform engineers and storage architects are feeling the squeeze: AI training datasets, analytics lakes and backup repositories are ballooning while SSD prices and supply uncertainty still dominate procurement conversations. SK Hynix’s late‑2025/early‑2026 announcement about a cell‑splitting approach for PLC flash is one of the first hardware advances intended specifically to make penta‑level cell (PLC) flash practical for datacenter tiers. For you, that means new tradeoffs in cost‑per‑GB, endurance, wear‑leveling behavior, and SSD lifecycle strategies—information you need now to design resilient, predictable storage tiers and CI/CD pipelines.

The evolution you need to know: PLC and the SK Hynix cell‑splitting idea

By 2026 the industry has broadly accepted the need to push more bits into each NAND cell to lower raw cost per gigabyte. PLC stores five bits per physical cell (vs. QLC’s four). That gets you higher density but traditionally at the cost of tighter voltage windows, higher error rates, and lower write endurance.

SK Hynix’s announced technique—commonly described in press as “chopping or cell‑splitting cells in two”—is an architectural compromise. Instead of treating each physical node as a single 5‑bit analog voltage window with 32 distinct states, the approach partitions (logically) the cell or its sensing circuitry so the effective number of charge states per accessible partition is reduced. The goal is to keep much of PLC’s density while relaxing voltage thresholds enough to improve error margins and endurance to a usable level for many datacenter workloads.

Think of it as turning a fragile 32‑state single channel into a pair of wider‑margin subchannels: you still get close to the same bits per area, but each subchannel behaves more like a high‑density QLC with lower raw error rates (and therefore improved achievable endurance after ECC and firmware compensation).

Why this matters in 2026: transparency, workloads and the AI storage shock

Two industry trends make this relevant now:

AI dataset scale: Organizations are storing petabytes to exabytes of model training data. Most training datasets are read‑heavy but still require stable, predictable capacity and acceptable read latency.
Cost pressure and supply dynamics: Late‑2025 supply constraints briefly pushed SSD $/GB higher; PLC offers the most aggressive path to lower raw $/GB if engineering hurdles are overcome.

For developers and sysadmins the implications are simple: new SSD SKUs using SK Hynix’s technique will likely be targeted at cold/warm object and dataset tiers where density and $/GB matter more than extreme write endurance. But because the internal behavior of these drives differs from existing TLC/QLC parts, you must adapt monitoring, tiering and lifecycle policies to avoid surprises.

Practical implications for storage tiers

Use the following pragmatic rules when introducing cell‑split PLC drives into your storage architecture:

Assign PLC primarily to cold and warm tiers, not hot transactional tiers.
PLC with cell‑splitting will likely target archival and dataset storage—places where capacity and read density dominate. Avoid using it for write‑intensive caches, metadata storage, or DB WALs unless the vendor provides convincing endurance numbers and SLC/DRAM caching strategies.
Adopt a multi‑tier strategy with transparent data movement policies.
Policy example: ingest and pre‑process data on NVMe TLC/QLC pools, move finalized datasets to PLC pools after validation and deduplication. Automate the movement using lifecycle rules (e.g., S3‑compatible lifecycle, Ceph tiering, or a Kubernetes CSI spot tier) and monitor latency SLAs.
Use PLC for read‑heavy AI training datasets, not for training checkpoints that are frequently overwritten.
Most large model training pipelines are read‑dominant on the dataset files; a checkpointing workload with frequent writes and overwrites will stress PLC’s write endurance. Consider hybrid approaches: PLC for raw dataset shards, TLC for scratch/checkpoints.

Sample storage tier matrix (high‑level)

Hot tier: NVMe TLC/enterprise SLC cache — low latency, high endurance
Warm tier: QLC/TLC enterprise SSDs — mixed workloads, moderate endurance
Cold/dataset tier: PLC w/ cell‑splitting — maximum capacity, read heavy

Wear‑leveling and endurance: what changes with cell‑splitting PLC

Wear‑leveling remains the most critical SSD controller responsibility. Cell‑splitting changes several inputs to your wear models:

Smaller effective voltage states per partition reduce raw bit error rates per partition but the overall device still retains the physics of PLC—so errors aren’t eliminated, just made more manageable.
Firmware complexity increases: controllers may use more aggressive ECC, dynamic SLC caching, and partition‑aware wear‑leveling to balance endurance across split subchannels.
Write amplification can change if garbage collection interacts poorly with split partitions; expect different GC timing and free‑space behavior.

Operationally, update your wear‑leveling and lifecycle playbook:

Set conservative overprovisioning.
Vendors may ship lower OP by default to maximize marketed capacity. For PLC pools, plan for higher OP—consider 15–30% spare capacity depending on workload and vendor guidance. Higher OP reduces write amplification and extends usable lifetime.
Prefer drives with robust telemetry and open SMART/NVMe counters.
Track attributes such as Percentage Used, Program Fail Count, Media Errors, and Available Spare. If the vendor adds partition‑specific counters for split cells, ingest those into your monitoring stack.
Simulate realistic P/E cycles in testbeds.
Before deploying at scale, run soak tests with workload profiles that mimic your production mix: mixed sequential reads, periodic large writes (ingest), and random small updates (metadata). Capture TWOTE (time‑weighted endurance) and variance across devices.

Example: nvme‑cli monitoring snippet

nvme smart-log /dev/nvme0 --output-format=json | jq '.

Key fields to monitor programmatically: data_units_written, data_units_read, warning_temp_time, , and vendor‑specific list entries. Automate thresholds and alerts in Prometheus/Grafana or your observability stack.

SSD lifecycle management: procurement, testing, and retirement

PLC with cell‑splitting will shift procurement conversations. Here are operational rules you can adopt:

Demand transparent endurance metrics and SLAs.
Ask vendors for explicit DWPD (drive writes per day), projected TBW at a confidence interval, and the failure modes observed during qualification. If possible, get test vectors from the vendor that match your workload (AI dataset reads, sequential ingest, metadata writes).
Negotiate firmware upgrade paths and remote diagnostics.
For new cell architectures, vendor supportability matters. Prioritize firmware upgrade paths, remote diagnostics and clear RMA processes that surface endurance degradation early.
Implement phased rollout and canary‑to‑production promotion policies.
Start with small pools, monitor three months of production behavior, then increase capacity. Use canary‑to‑production promotion policies and track deviations in SMART attr deltas.
Automate retirement metrics.
Define retirement triggers beyond raw P/E cycles: abnormal increases in uncorrectable errors, rising program/erase failure rates, or unexplained jump in write amplification. Integrate into asset management workflows to ensure graceful evacuation and rebuild.

Lifecycle policy YAML (example)

lifecycle:
  tiering:
    - name: hot
      storage_class: nvme_tlc
      max_age_days: 7
    - name: warm
      storage_class: qlc
      max_age_days: 90
    - name: cold
      storage_class: plc_cell_split
      max_age_days: 365
  retirement_triggers:
    - smart_percent_used >= 85
    - media_errors.delta_per_day > 100
    - program_fail_count > 10

Operational patterns and best practices

Below are specific, actionable patterns you can apply immediately.

Use S3/HTTP object tiering for dataset immutability.
Store master copies of training datasets as immutable objects with checksums (e.g., SHA‑256). Immutable objects reduce small random updates and therefore reduce wear on PLC pools.
Favor large, aligned writes.
Aggregating writes into larger sequential batches reduces write amplification. For ingestion pipelines, buffer small writes locally and flush in larger aligned blocks—especially important when writing to PLC.
Enable erasure coding instead of small‑stripe RAID for density.
Erasure coding gives you lower storage overhead at comparable reliability to RAID‑6 and reduces rebuild bandwidth by enabling flexible reconstruction—helpful when rebuilds may be more frequent at scale for low‑cost drives.
Provision larger rebuild and GC windows.
PLC drives may need more latency headroom during internal garbage collection. Avoid aggressive rebuild concurrency; stagger rebuilds to prevent cascading performance impacts.

AI training storage: where PLC fits and where it doesn’t

AI training setups have two primary storage concerns: capacity for datasets and throughput for feeding GPUs/TPUs.

Datasets (read‑heavy):
PLC is well suited for large, read‑heavy dataset repositories. A typical training job reads shards repeatedly; once written, dataset shards see infrequent writes. The cell‑splitting approach improves the viability of PLC for this role because it improves achievable raw BER (bit error rate) and reduces the need for excessive ECC overhead.
Training scratch and checkpointing (write‑heavy):
Do not use PLC for high frequency checkpoints or parameter server volumes unless the specific drive advertises sufficient endurance. Use TLC NVMe or PMEM for checkpoint hot paths.
Caching and tiered feeders:
Use NVMe front‑end caches (DRAM or SLC cache) to shape access patterns into the PLC pool. Prefetch hot shards onto faster media during the training window; offload cold shards to PLC after the job completes.

Benchmarks and testing—what to run now

Before buying PLC drives, run a compact, repeatable test matrix:

Sequential read throughput: Measure sustained read bandwidth across multiple parallel readers to simulate multi‑worker training jobs.
Mixed read/write (70R/30W) with file sizes matching your workload: Track IOPS, tail latency, and error rates.
P/E cycle soak: Run limited P/E cycle tests up to vendor DWPD claims; measure ECC correction counts and uncorrectable errors.
GC and rebuild stress: Execute forced trim/Garbage Collection and then trigger node rebuilds while measuring latencies across tiers.

Document results, and insist on vendor transparency if results differ significantly from marketing specs.

Security, compliance and data integrity

PLC’s hardware changes do not directly alter your logical security posture—but two areas deserve attention:

Integrity checks and checksums: Add end‑to‑end checksums (FASTQ/TFRecord level, object-level checksums) to detect latent bit errors. Relying solely on controller ECC can mask errors until they cause logical corruption.
Auditability and vendor attestations: Require vendor documentation for wear‑leveling algorithms, error‑reporting semantics and firmware update procedures to satisfy auditors. For regulated workloads, insist on documented UBE/UBER behavior at scale and retention of SMART telemetry for audits.

Future predictions and what to watch in 2026–2027

Based on current trends, expect the following:

Tiered PLC SKUs emerge: Vendors will ship PLC models tuned for specific roles—read‑optimized vs. balanced—visible in their telemetry and endurance claims.
Controller innovations: Expect more aggressive partition‑aware GC and ECC schemes that will further narrow the endurance gap between PLC and QLC/TLC.
Standards and telemetry: Industry groups and hyperscalers will push for more granular SSD telemetry (partition‑level health metrics) to make PLC adoption safer at scale.
Price parity pressure: As more suppliers ship PLC, market $/GB should fall—particularly for cold/warm tiers—helping ease AI storage costs by late‑2026 to 2027.

Checklist: How to prepare your stack for PLC with cell‑splitting

Run a small proof‑of‑concept with realistic workloads and telemetry collection.
Raise overprovisioning on PLC pools and define retirement triggers for drives.
Use immutable objects and larger write aggregation to reduce small random writes.
Require vendor DWPD, TBW, and explicit PLC partition health counters before procurement.
Integrate NVMe SMART and vendor telemetry into Prometheus/Grafana and your incident runbooks.
Prefer erasure coding for capacity efficiency and lower rebuild cost compared to traditional RAID for PLC pools.

Final takeaways: where to bet and when to be cautious

SK Hynix’s cell‑splitting approach is not a magic bullet, but it is a pragmatic hardware innovation that makes PLC a realistic option for large, read‑heavy storage tiers in 2026. For developers and sysadmins the immediate opportunities are:

Lowered cost‑per‑GB for cold/warm dataset pools if vendors deliver on endurance and telemetry.
New procurement and lifecycle management disciplines: more monitoring, higher overprovisioning and staged rollout.
Clear design patterns for AI training architectures—PLC for dataset bulk storage; TLC/QLC/NVMe for checkpoints and hot IO.

Be cautious about treating PLC as a straight swap for your existing SSDs. The technology changes the inputs to wear‑leveling, garbage collection and lifecycle. With a disciplined testing and monitoring approach you can capture the cost benefits while avoiding unexpected failures.

Actionable next step: Create a one‑week test plan that exercises your ingest and training read workloads against evaluation PLC drives, collect SMART/NVMe telemetry, and compare rebuild and GC latency to your current storage. Use the checklist above as your audit template.

Call to action

If you manage storage for AI pipelines, analytics, or large‑scale backups, start a controlled pilot today. Demand transparent endurance metrics from vendors, instrument drives with end‑to‑end telemetry, and update lifecycle automation. Want a ready‑made test plan and Prometheus dashboards to evaluate PLC drives with SK Hynix’s cell‑splitting architecture? Contact our engineering team for a prebuilt test harness, dashboard templates and a vendor negotiation checklist tailored to enterprise procurement.

oracles

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.