Wafer | LLMs for enterprise

The fastest open source LLMs for enterprise.

dedicated The inference your product actually needs Bring us the model, traffic shape, and SLO. Wafer builds the endpoint around them—and keeps optimizing after it goes live. Tailored to your traffic Your real request mix — not a generic benchmark — drives batching, caching, routing, and decode decisions The whole stack searched Model, engine, kernels, and hardware are optimized together against the outcome you care about Reliable by design Every candidate must preserve correctness and meet your reliability targets before it can ship Never finished optimizing When traffic shifts,…

saved by

• Ratan Kaliani
• Chris Cardoza

related reading

• LLM Engineer's Almanac - Advisormodal.com
• LLM Inference Performance Engineering: Best Practices | Databricks Blogdatabricks.com
• Looking back at speculative decodingresearch.google
• KernelBench: Can LLMs Write GPU Kernels?scalingintelligence.stanford.edu
• Together AI | The AI Native Cloudtogether.ai
• Best practices to accelerate inference for large-scale production workloadstogether.ai
• ⭐️ Fast LLM Inference From Scratchandrewkchan.dev
• LLM Inference Handbookhandbook.modular.com
• GitHub - wafer-ai/gpu-perf-engineering-resources: A curated resource list for learning AI performance engineering, from GPU fundamentals to production inference.github.com
• AI Model & API Providers Analysis | Artificial Analysisartificialanalysis.ai
• Reiner Pope – The math behind how LLMs are trained and serveddwarkesh.com
• Optimizing inference · Hugging Facehuggingface.co