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Rank #133 · on radar since 2026-07-03

Efficient-LLM-Inference-Serving-Systems

Why is LLM inference slow — and how do you make it fast? A hands-on, first-principles course: roofline → KV cache → quantization → parallelism → vLLM/SGLang, with GPU labs on open models.

Visit homepage ↗cudainference-optimizationsglangflash-attention+16GitHub

Momentum

87.5
24h7d

Why it's ranked

Every score decomposes into published factors — the same math for every tool, paid or not. Read the methodology →

Velocity (weighted, cohort-normalized)0.908
Signal decay0.995
Corroboration1.000
Quality gate1.000

Raw signals (30 days)

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