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Learn how GPT-6 improves prompt caching with higher cache hit rates, new diagnostics, explicit breakpoints, and controls that reduce latency and costs.
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That is embarrassing onstage. In production, it is a reliability problem: a workflow that succeeded once may fail the next time a user makes the same request. For mission-critical work, such as reconciling a financial transaction or checking a contract for an obligation, that can be a showstopper.
Structured output is one of the most common real-world tasks for LLMs, yet most benchmarks fold it into broader reasoning or extraction scores rather than measuring it on its own. Whether a model reliably returns valid, parseable output in the requested format and shape — schema compliance — is often what decides whet…
As models become faster and workloads scale, that balance begins to shift. Training on massive datasets, serving many concurrent requests, or repeatedly processing long inputs can put enough pressure on the tokenizer that it starves the model of data.
Running AI models on your laptop has become much easier, and llama.cpp has been a big part of that. Its inference engine powers local AI tools such as Ollama, LM Studio, and Jan. Alongside projects like MLX , it has helped make local inference a practical option for everyday use.