4 posts tagged with "Performance"

Knowledge distillation is a model-compression and capability-transfer technique in which a powerful teacher model provides training signals for a smaller student model.

Instead of requiring the student to rediscover every useful behaviour from raw internet-scale pretraining data, the student learns from the teacher’s outputs, probability distributions, internal representations, reasoning demonstrations or preferences.

A distilled model can therefore become:

• Faster at inference
• Less expensive to operate
• Smaller in memory
• Easier to deploy on limited hardware
• More specialised for a particular task
• More consistent than the original general-purpose model for a narrow workflow

However, an important distinction must be made:

The exact internal process used to train current proprietary Claude models is not publicly disclosed in full.

Anthropic publishes model reports, safety research and selected training information, but its public materials do not expose Claude’s weights, token logits, hidden states, training datasets or the complete teacher–student training recipe. Therefore, when people discuss “distilling Claude,” they may be referring to two different things:

1. Authorised distillation within an official platform, such as Amazon Bedrock’s documented Claude Sonnet-to-Haiku distillation workflow.
2. Black-box behavioural distillation, where permitted Claude outputs are collected and used to fine-tune another model.

Anthropic and AWS have publicly described an authorised workflow in which Claude 3.5 Sonnet generates synthetic training data, Claude 3 Haiku is trained and evaluated using that data, and the resulting distilled model is hosted for inference. This is primarily output-based behavioural distillation rather than traditional access to Claude’s internal logits or hidden layers.

LLM inference is expensive because the model repeatedly recomputes attention state over the same tokens. Caching attacks that redundancy at every layer of the stack: inside one decode loop (KV cache), across concurrent requests on a serving GPU (PagedAttention / prefix caching), across API calls for stable prompt prefixes (prompt caching), and finally at the application layer when the answer itself can be reused (exact / semantic response caching).

This guide synthesizes the mechanics from Sebastian Raschka’s KV-cache and architecture work, Sankalp’s PagedAttention deep dive, provider prompt-caching docs, AWS caching patterns, and application-layer guides from Machine Learning Mastery, IBM, Latitude, ngrok, and related sources into one engineering playbook.

Generative AI cost management is more complicated than ordinary cloud cost management. A conventional application might be charged according to CPU hours, memory, storage, and network usage. An LLM application can accumulate costs through input tokens, output tokens, cached tokens, reasoning tokens, embeddings, retrieval, reranking, guardrails, model evaluation, agent tool calls, retries, observability, and the infrastructure supporting the application.

The main FinOps challenge is therefore not simply:

How can we use the cheapest model?

The correct question is:

What is the lowest sustainable cost at which the application can meet its quality, latency, reliability, security, and business-outcome requirements?

Caching in a conventional application usually means storing the result of a database query or API request. In an LLM application, there are many more opportunities: repeated questions, equivalent paraphrases, identical embeddings, repeated retrieval and reranking, repeated tool calls, stable system prompts, and recurring agent planning steps.

The strongest architecture therefore does not implement one “LLM cache.” It implements a hierarchy of specialised caches across the RAG and agent pipeline.