In the last two weeks GPU memory virtualization and disaggregation moved from infra curiosity to a rapid, production trend—because models and simulations increasingly need tens to hundreds of gigabytes of VRAM. Read this and you'll know what's changing, why it matters to your AI, quant, or biotech workloads, and what to do next. The core idea: software‐defined pooled VRAM—virtualized memory, disaggregated pools, and communication‐optimized tensor parallelism—makes many smaller GPUs look like one big memory space. That means you can train larger or more specialist models, host denser agentic workloads, and run bigger Monte Carlo or molecular simulations without buying a new fleet. Tradeoffs: paging latency, new failure modes, and security/isolation risks. Immediate steps: profile memory footprints, adopt GPU‐aware orchestration, refactor for sharding/checkpointing, and plan hybrid hardware generations.

Are you quietly burning multi‐million dollars a year on LLM inference while latency kills real‐time use cases? In the past 14 days (FinOps reports from 2025‐11–2025‐12), distillation, quantization, and edge NPUs have converged to make “right‐sized AI” the new priority — this summary tells you what that means and what to do. Big models (70B+) stay for research and synthetic data; teams are compressing them (7B→3B, 13B→1–2B) and keeping 90–95% task performance while slashing cost and latency. Quantization (int8/int4, GGUF) and device NPUs mean 1–3B‐parameter models can hit sub‐100 ms on phones and laptops. Impact: lower inference cost, on‐device privacy for trading and medical apps, and a shift to fleets of specialist models. Immediate moves: set latency/energy constraints, treat small models like APIs, harden evaluation and SBOMs, and close the distill→deploy→monitor loop.

On 2025-11-08 CRISPR Therapeutics reported Phase I results from a 15‐participant, single‐dose in‐vivo ANGPTL3 gene‐editing trial in the UK, Australia and New Zealand showing ~50% reductions in LDL cholesterol and triglycerides lasting at least 60 days; one participant with preexisting heart disease died but the death was not attributed to the treatment and no serious adverse events were linked to the therapy, and Phase II is planned for 2026. Separately, VERVE‐102 (PCSK9 base editing) delivered average LDL reductions of 53% (up to 69% in the highest dose) after one dose and was well tolerated. These early, durable effects move gene editing into common cardiovascular/metabolic markets and prioritize Phase II and long‐term follow‐up data, regulatory pathways, manufacturing scale‐up and pricing/reimbursement strategies.