Chroma

The Small Model Infrastructure Nobody Built (So We Did) — Filip Makraduli, Superlinked

May 5, 2026 · 18:30

Filip Makraduli of Superlinked introduces SAI, an open-source inference engine for small models that addresses gaps in embedding infrastructure by enabling dynamic model loading, hot-swapping, and memory-aware eviction on a single GPU. He argues that provisioning separate GPUs for each small model wastes idle capacity, and that the real challenge lies in supporting diverse model architectures (e.g., BERT, Qwen, Colbert) with different attention mechanisms and positional embeddings. The engine re-implements forward passes with variable-length FlashAttention and handles model swapping via a least recently used eviction policy. Makraduli also explains that context management for agents requires small models to pre-process data, referencing Andrej Karpathy’s graph-based knowledge bases and Chroma’s own model. The talk details the infrastructure layer including routing, auto-scaling with Prometheus, and GPU provisioning using spot instances, all open-sourced as SAI (Superlinked Inference Engine) with Helm charts and Docker images.

How to look at your data — Jeff Huber (Chroma) + Jason Liu (567)

Aug 6, 2025 · 19:23

Jeff Huber (Chroma CEO) and Jason Liu argue that AI practitioners should look at their data—both inputs and outputs—with fast evals and conversation analysis to systematically improve retrieval and product decisions. Huber advocates for fast evals using golden datasets of query-document pairs over public benchmarks or expensive LLM judges, showing how synthetic queries aligned to real user behavior can empirically compare embedding models; in a Weights & Biases chatbot case study, Voyage 3 large outperformed text-embedding-3-small and others. Jason Liu focuses on outputs: extract structured metadata from conversations, cluster them to find segments, then compare KPIs like performance across clusters to prioritize what to fix, build, or ignore—for example, if 40% of conversations involve data visualization and the agent performs poorly, invest in better plotting tools. This population-level analysis enables impact-weighted decisions, turning vague metrics like 0.5 factuality into actionable insights by segment. The episode emphasizes that retrieval improvements are foundational, and once you have users, looking at conversation structure drives a data-driven product roadmap.