Deepseek research touts memory breakthrough... | Tom's Hardware

DeepSeek Engram: A Game‑Changer for Enterprise LLM Scaling – 2026 { "@context": "https://schema.org", "@type": "Article", "headline": "DeepSeek Engram: A Game‑Changer for Enterprise LLM Scaling – 2026", "datePublished": "2026-01-12", "dateModified": "2026-01-12", "author": { "@type": "Person", "name": "Senior Technology Journalist" }, "publisher": { "@type": "Organization", "name": "Enterprise AI Insights" } } DeepSeek Engram: A Game‑Changer for Enterprise LLM Scaling – 2026 Executive Snapshot: DeepSeek’s Engram conditional‑memory module redefines how large language models (LLMs) handle static knowledge, freeing GPU high‑bandwidth memory (HBM) for dynamic inference. In practical terms, enterprises can now deploy up to 27 B‑parameter models on single‑node GPUs while cutting HBM costs by ~40%, improving throughput by 18% and reducing per‑inference power draw by 20 W. The open‑source integration layer positions DeepSeek as a viable alternative to GPT‑4o and Gemini‑1.5 for cost‑conscious, compliance‑heavy workloads. Strategic Business Implications The memory bottleneck has long been the hidden cost driver in LLM scaling. As of early 2026, cloud providers report that HBM upgrades represent up to 30% of an inference node’s capital expenditure (CapEx). Engram flips this narrative: by moving static knowledge to commodity DDR5 or CXL‑enabled RAM, enterprises can: Lower CapEx and OpEx: A single NVIDIA RTX‑6000 H100 GPU with 80 GB HBM costs ~$15k; replacing 40% of that stack with DDR5 cuts the upfront cost to ~$9k, while ongoing power savings translate to $1.2k per year on a typical 8‑GPU node. Accelerate Time‑to‑Market: Faster inference (≈20 ms token latency reduction) enables real‑time analytics and conversational agents that were previously only feasible in batch modes. Enhance Compliance: Keeping static knowledge on system RAM simplifies data residency controls, a critical requirement for regulated sectors such as finance and healthcare. The ability to isolate RAM within o