Inference system design is increasingly the real bottleneck in production AI deployments, not model capability. Teams often default to fine-tuning when outputs are inconsistent, but the root cause is frequently in the retrieval layer, context management, or task routing. Key issues include uniform compute allocation regardless of task complexity, poorly calibrated retrieval rankers, and context window bloat that degrades reasoning quality. Techniques like speculative decoding, context compression, and compute routing are becoming critical engineering concerns. As capable foundation models commoditize, the differentiator is how well the surrounding inference infrastructure is designed.
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Fine-tuning is useful, but it gets overusedWhat’s happening at inference timeThe resource allocation problemThese systems are more layered than people realizeMemory is becoming a real issueTakeaway3.2K Impressions