Is QLC Flash the Sweet Spot for AI Storage?

AI storage needs more than capacity. It needs enough performance to keep GPUs fed, enough density to fit within real data-center constraints, and enough endurance to survive demanding workloads without destroying the economics. In Episode 14 of the Scale Out Podcast, Scality CTO and founder Giorgio Regni joins CMO Paul Speciale to unpack new testing performed with Samsung at its MAX Lab. The focus: whether high-density QLC flash can deliver the right balance of performance, durability, density, and total cost for AI storage. The team tested 61 TB Samsung QLC SSDs beneath Scality's object-storage architecture on a 400Gb network. The results show why QLC can be a compelling alternative to HDD for performance-intensive AI workloads — while also revealing why software architecture matters enormously for flash endurance. Key takeaways: Why AI inference and training can demand far more performance than HDD alone can practically deliver How QLC compares with HDD for random I/O and throughput-intensive AI workloads Why Scality's sequential-write architecture is naturally well suited to QLC endurance How the test measured just 1.46× write amplification under the Scality workload Why a failed 61 TB SSD does not create the same rebuild bottleneck as traditional RAID How higher-density QLC can reduce floor space, CPU requirements, power, and cooling Why $/TB alone is the wrong metric for AI storage — and when performance TCO favors QLC Chapters: 00:00 Testing QLC Flash for AI Storage 01:07 Inside Samsung's MAX Lab 02:04 Why HDD Alone Falls Short for AI Performance 04:09 Designing Object Storage for QLC Durability 05:35 Turning Random Workloads Into Sequential Writes 07:19 Measuring QLC Write Amplification 08:49 Rebuilding a 61 TB QLC Drive 10:21 Density, Power, and Data-Center Efficiency 11:06 Why $/TB Is the Wrong AI Storage Metric 13:17 The QLC vs HDD Break-Even Point #Scality #AIInfrastructure #QLC