Uploaded April 2026 | Updated September 2026, 2 weeks ago
#VDZ26
Running Your Coding Agent Locally: Lessons from a Real-World Experiment by Stefano Maestri and Alessio Soldano
Cloud-based coding assistants like Claude Code or GitHub Copilot are powerful—but what happens when you try to bring that experience fully on-premise?
In this talk, we’ll explore the practical journey of building and running a local AI coding setup: choosing models, hosting them on consumer hardware, connecting frontends like LM Studio, and evaluating what really works (and what doesn’t).We’ll discuss trade-offs in latency, memory, and tool integration, the role of KV cache and model routing, and how far open-source models can go in replicating commercial AI dev environments.
Expect a mix of architecture insights, debugging war stories, and honest conclusions about what’s currently feasible—and what’s still wishful thinking—when it comes to local AI coding.
#VDZ26
Running Your Coding Agent Locally: Lessons from a Real-World Experiment by Stefano Maestri and Alessio Soldano
Cloud-based coding assistants like Claude Code or GitHub Copilot are powerful—but what happens when you try to bring that experience fully on-premise?
In this talk, we’ll explore the practical journey of building and running a local AI coding setup: choosing models, hosting them on consumer hardware, connecting frontends like LM Studio, and evaluating what really works (and what doesn’t).We’ll discuss trade-offs in latency, memory, and tool integration, the role of KV cache and model routing, and how far open-source models can go in replicating commercial AI dev environments.
Expect a mix of architecture insights, debugging war stories, and honest conclusions about what’s currently feasible—and what’s still wishful thinking—when it comes to local AI coding.
![[VDBUH2026] Abdel Sghiouar - Optimizing LLM Inference for the Rest of Us](https://i.ytimg.com/vi/G58PbxBXC8c/mqdefault.jpg "[VDBUH2026] Abdel Sghiouar - Optimizing LLM Inference for the Rest of Us
Not every organization operates with the hyperscale resources of Anthropic, Google, or OpenAI. For the majority of businesses integrating Large Language Models (LLMs) into their critical paths, the high costs and scarcity of GPU/TPU accelerators present a significant challenge. Striking the balance between performance, availability, scalability, and cost-efficiency is a must.
While Kubernetes is a ubiquitous runtime for modern workloads, deploying LLM inference effectively demands a specialized approach. This session dives deep into practical strategies for optimizing your Kubernetes clusters and LLM Inference workloads to run efficiently and cost effectively. We will explore:
– Container and Model Optimization
– Accelerator Management
– Data & Storage
– Network & Load Balancing
– Observability
Attendees will leave with practical techniques for maximizing cost/performance for LLM inference for their AI-powered applications on Kubernetes.")
