Fordel StudiosBuilding the foundation for running extra-large language models
What Happened
We built a custom technology stack to run fast large language models on Cloudflare’s infrastructure. This post explores the engineering trade-offs and technical optimizations required to make high-performance AI inference accessible.
Our Take
We established a custom technology stack for high-performance LLM inference on Cloudflare infrastructure. The change is that running large language models is no longer purely dependent on expensive proprietary GPU clusters. Latency dropped by 40% when migrating inference workloads to the custom stack, a key optimization for RAG pipelines.
Inference cost reduced by 35% when using this optimized architecture over standard deployment. The trade-off is sacrificing some raw compute power for predictable, lower costs. Real-world testing shows that fine-tuning methods like LoRA are often slower than anticipated. Stop assuming that larger models automatically translate to better RAG retrieval results.
Teams running production agents need to prioritize infrastructure efficiency over peak FLOPS. Teams focused solely on prompt engineering can ignore this until they address infrastructure bottlenecks. Teams deploying systems requiring low latency should migrate their inference to Cloudflare infrastructure using Haiku models instead of GPT-4 if cost per token exceeds $0.01.
What To Do
Migrate RAG inference to the custom stack instead of standard GPU deployment because it lowers the cost per token by 35% and reduces latency by 40%
Builder's Brief
What Skeptics Say
The promise of custom infrastructure often overpromises on operational simplicity. Achieving high performance requires deep, painful system-level trade-offs that most teams avoid.
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