SK Hynix Q3 profit soars 62% on AI memory chip demand

SK Hynix reported a 62% rise in operating profit for the September quarter, reaching a record 11.4 trillion won (US$8 billion), slightly above analyst expectations.

Revenue climbed to 24.5 trillion won (US$17.1 billion) as strong demand for AI infrastructure drove sales of its memory chips.

The company said its entire DRAM and NAND lineup for 2026 is already fully booked.

SK Hynix plans to ramp up production and will start supplying next-generation HBM4 chips this quarter, though it did not name its customers.

Shares rose as much as 4.1% in premarket trading in Seoul.

Analysts link the surging demand to large-scale AI projects and orders from hyperscalers.

SK Hynix and Samsung have also raised traditional memory chip prices by up to 30% for Q4, while some investors remain cautious about the sustainability of the AI-driven boom.

🔗 Source: Bloomberg

🧠 Food for thought

Implications, context, and why it matters.

HBM4 shift and packaging bottlenecks will shape SK hynix profits through 2026

SK hynix’s record profit hinges on a 62% Q2 HBM share 1. Samsung could pass 30% in 2026 with HBM3E certification and an HBM4 ramp 2.
Micron is shipping HBM4 samples up to 11 Gbps and targets an ~$8 billion annualized HBM run rate 1. SK hynix lists 10 Gbps with a 40% power-efficiency gain, while Samsung aims for 11 Gbps 1 2.
Supply can lag as foundries and packaging houses add Chip-on-Wafer-on-Substrate (CoWoS) lines and suppliers lift Through-Silicon Via (TSV) yield 3. A seven-step stack using Mass Reflow Molded Underfill (MR-MUF) and Thermal Compression with Non-Conductive Film (TC-NCF) still creates bottlenecks despite full-booking claims 4.

Memory efficient software can blunt HBM prices and DRAM hikes for AI accelerators

DRAM prices rose up to 30% for Q4, while HBM revenue could grow from $17 billion in 2024 to $98 billion by 2030 4.
vLLM, an open-source inference engine for large language models, uses chunked prefill and parallelism to raise throughput and cut latency 5. LeanKV compresses the transformer key-value cache by 2.7x to 5.7x with throughput gains of 1.9x to 5.4x 6.
GPU vendors and cloud providers face HBM supply concentration above 90% in Korean hands in 2024 4. Bundling memory-efficient inference stacks with hardware can reduce that exposure and strengthen bids 4.