Tutorial: Powering Agentic Inference with @SambaNovaSystems | Agentic AI Conference

Why It Matters

Accelerated, low‑cost inference enables enterprises to run always‑on AI agents at scale, turning latency‑bound workflows into competitive advantages.

Key Takeaways

• SambaNova’s SM50 chip delivers >1,000 tokens/sec on 8B models.
• Fast inference reduces agent workflow latency from tens to minutes.
• Energy‑efficient racks enable scaling agents without massive data‑center footprint.
• BYOC support lets customers import fine‑tuned models onto SambaNova.
• Open‑source starter kits and Helm charts simplify on‑prem deployment.

Summary

The tutorial highlighted SambaNova’s strategy for solving the "agentic inference" infrastructure crisis by showcasing its end‑to‑end stack—from custom SM50 silicon to cloud‑run deployments. Quasian Koma, director of AI solutions engineering, explained how the company’s full‑stack platform combines low‑power racks, high‑throughput inference, and a BYOC model‑import framework to meet the growing demand for always‑on agents that consume massive token volumes. Key insights included the SM50 chip’s ability to process around 1,100 tokens per second on an 8‑billion‑parameter model, delivering a dramatic speed advantage over conventional GPUs. The platform’s energy‑efficient design allows multiple large models to run on a single rack, cutting total cost of ownership while supporting custom checkpoints via the BYOC approach. Market trends—such as Anthropic’s premium pricing for fast mode and Nvidia’s hardware‑split prefill/decode—underscore the premium placed on low latency compute. During the live demo, Koma ran a MiniAx data‑analysis pipeline that ingested a Kaggle‑style dataset, generated code, executed it, and produced an eight‑page report in under two minutes. This contrasted sharply with typical GPU‑based workflows that can take 30‑40 minutes for comparable tasks, illustrating how high token‑per‑second rates compress end‑to‑end latency for complex, multi‑step agentic pipelines. The implications are clear: enterprises can now deploy autonomous agents at scale without prohibitive infrastructure costs, unlocking faster decision‑making, higher throughput, and new business models that rely on continuous AI assistance. SambaNova’s open‑source starter kits and Helm charts further lower the barrier for on‑prem adoption, positioning the company as a viable alternative to traditional GPU providers.

Original Description

This hands-on lab by Kwasi Ankomeh, Director of AI Solutions at SambaNova, shows how the next frontier of AI isn't just about bigger models—it's about agents that can think, plan, and act autonomously. But behind every intelligent agent lies an infrastructure crisis: traditional hardware wasn't built for the unpredictable, multi-step workloads of agentic AI.

This presentation discusses the SN50™, SambaNova's answer to this challenge. By combining a purpose-built dataflow architecture, innovative agentic caching for ultra-fast model switching, and cloud-scale deployment supporting up to 256 accelerators and 10 trillion parameter models, we're enabling enterprises to deploy agentic inference that's not just faster—it's finally cost-effective. Join us to explore the future of inference infrastructure.

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