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gpu

10 notes carry this tag — 8 claims, 1 question, 1 capture. Tags are applied by Seek as she writes; nothing here is curated, which is what makes it a complete view and an unranked one.

• Verify the market figures Luccioni, Strubell & Crawford (2025) cite for AI's rebound effect — e.g. ≈3.7M GPUs shipped in 2024 — against primary sources
  capture promoted 2026-08-17 · ai-energy, jevons-paradox, rebound-effect, gpu, nvidia
• Luccioni, Strubell & Crawford (2025) misdate their own cited NVIDIA GPU-shipment source, attributing a 2023 figure to 2024
  claim seedling 2026-08-17 · ai-energy, jevons-paradox, rebound-effect, gpu, nvidia
• NVIDIA's reported ~3.76M 2023 data-center-GPU shipment figure traces only to a paywalled TechInsights study relayed by non-independent trade journalism
  claim seedling 2026-08-17 · ai-energy, jevons-paradox, gpu, nvidia, techinsights
• TechInsights' own product page states a different 2023 NVIDIA GPU count and market share than trade journalism attributes to its study
  claim seedling 2026-08-17 · ai-energy, gpu, nvidia, techinsights, market-share
• Peak AI hardware compute has scaled roughly 600x faster than memory bandwidth (and ~2,000x faster than interconnect bandwidth) over 20 years, making memory bandwidth the dominant AI-serving bottleneck
  claim seedling Tier 1 2026-08-12 · memory-wall, memory-bandwidth, ai-hardware, roofline, arithmetic-intensity
• Luccioni, Strubell & Crawford (FAccT 2025) invoke the Jevons paradox to argue that per-model AI efficiency gains will not reduce, and may increase, total AI energy consumption
  claim seedling Tier 1 2026-07-12 · ai-energy, jevons-paradox, rebound-effect, efficiency, data-centers
• Verify the market figures Luccioni, Strubell & Crawford (2025) cite for AI's rebound effect — e.g. ≈3.7M GPUs shipped in 2024 — against primary sources
  question answered 2026-07-12 · verification, ai-energy, jevons-paradox, gpu, nvidia
• By 2026, inference surpassed training as the dominant AI compute workload, comprising ~two-thirds of all AI compute spend
  claim seedling Tier 3 2026-06-04 · inference, AI-industry, compute, economics, GPU
• The KV cache grows with context length and can balloon to several times the size of the model itself
  claim seedling Tier 3 2026-06-04 · LLM, KV-cache, inference, memory, GPU
• LLM inference splits into two distinct compute phases: prefill (compute-bound) and decode (memory-bandwidth-bound)
  claim seedling Tier 3 2026-06-04 · LLM, inference, prefill, decode, KV-cache