Achieving Extreme Efficiency through Specialized GPU Kernel Generation
Summary
Automated GPU kernel generation produced Qwen 3.5 122B kernels that are 1.8–5.2× faster than the top implementations available in vLLM. Achieving these efficiency gains requires pairing unconstrained agent exploration with a strict outer verification system to catch measurement bugs and validate real-world speedups.
Summary generated by brickster.ai. For the full article, follow the source link above.
More from Databricks Blog
Five ways marketers can use Genie One
Genie One enables marketing teams to translate governed data into real-time insights and uncover cross-channel performance trends without relying on manual reporting. By independently exploring customer and pipeline data, teams can accelerate strategy speed-to-market and maximize campaign ROI.
Governance beyond security: knowledge, context & ontology on the lakehouse
Existing governance artifacts like classification tags, data contracts, and lineage provide the semantic foundation needed to run catalog-centered AI agent lifecycles directly within Unity Catalog. Anchoring this business context in catalog metadata keeps production PHI within governed boundaries while allowing cheaper models to deliver trusted results.
Building High-Quality and Trusted Data Products with Databricks
IntroductionOrganizations aiming to become AI and data-driven often need to provide...
What is Data Transformation?
Effective data transformation turns raw, inconsistent data into trusted, analytics-ready datasets through foundational operations like cleansing, validation, and enrichment. Scaling these workloads across batch and streaming requires combining robust ETL patterns and performance optimizations with modern tooling like Spark Declarative Pipelines and Lakeflow Jobs.
Southern Company’s SCOUT: Completing the Storm Intelligence Story
Southern Company completed its end-to-end storm intelligence architecture by deploying SCOUT on the Databricks Data + AI Platform, unifying outage, customer, terrain, and crew-planning data into a real-time restoration application. Built on Unity Catalog, Delta Lake, and collaborative notebooks, the solution couples near-real-time data ingestion with governed analytics while leveraging Databricks Genie Code to accelerate pipeline development.
Expanding Genie Agents: Deep analysis, file reasoning, and more
Genie Agents now feature an API-accessible Agent mode for multi-step analysis and support reasoning across unstructured files stored in Unity Catalog volumes alongside structured data. Teams can also leverage Genie Code to streamline agent curation, configure custom instructions, diagnose performance, and manage agent quality.