An FDE’s playbook for diagnosing TTFT delays
Most discussions about AI inference performance focus on model speed. In practice, however, users experience performance through a different metric: Time to First Token (TTFT).…
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Accelerating LLM Inference at the Edge: The Case for Weight Matrix Surgery
LASER: LAyer SElective Rank-Reduction Core Idea LASER introduces a counterintuitive finding: rather than adding capacity to a large language model, you can often improve…
Unlocking the Speed of AI: A Deep Dive into vLLM
Generative AI models are incredibly powerful, but running them at scale comes with a massive, hidden challenge: they can be incredibly slow and expensive…
Inference is Underrated
Scan the AI headlines and you'd be forgiven for thinking the only thing that matters is the next training run. $5 billion clusters. Millions…
Has the industry over-optimized for model intelligence while under-engineering inference operability?
As AI adoption accelerates, the industry narrative is increasingly dominated by model novelty, benchmark performance, and rapid feature velocity. However, practitioners operating real-world inference…
Why BitNet Could Reduce LLM Inference Cost by ~100× at Scale
BitNet is a neural network architecture that uses extremely low precision weights (around 1–1.58bits) instead of traditional floating point numbers. This change dramatically reduces…
What makes Inference inevitable
What makes Inference inevitable