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…
Community Field Notes
Built in production. Shared by the community.
Ideas, experiments, and hard-won lessons from the people building AI inference. Explore practical insights on vLLM, llm-d, GPUs, and open infrastructure—and share what you learn.
The Next Phase in Enterprise AI: System-Level Intelligence with vLLM Semantic Router
As enterprises move from experimental AI deployments to large-scale production, a stark reality has emerged: inference—specifically, simply throwing larger models…
SLO Latency Governance in AI : Multi tier Architecture
NVIDIA Blackwell and NVFP4 fundamentally change the economics of LLM inference by dramatically increasing effective HBM capacity and enabling much…
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,…
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…
Inference is Underrated
Scan the AI headlines and you'd be forgiven for thinking the only thing that matters is the next training run.…
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,…
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.…
What makes Inference inevitable
What makes Inference inevitable