A100 (Product): news timeline & CVEs

Productive, Durable, Fungible: How NVIDIA AI Factories Maximize Return on Investment

NVIDIA argues AI factories maximize ROI through higher tokens per megawatt, longer GPU life, and workload fungibility.

NVIDIA says AI factory operators spend about $60 million per megawatt and should judge returns by earning capacity, useful hardware life, and token demand. Citing SemiAnalysis AgentX, it claims Vera Rubin NVL72 delivers over 30 times the throughput per megawatt of GB300 NVL72 and up to 45 times lower cost per million tokens on DeepSeek V4 Pro. The company notes A100 GPUs shipped in 2020 remain in service, with CoreWeave bookings extended through 2029, and cites longer lives at major operators, including Microsoft's V100 fleet at 8.4 years.

NVIDIA Blog13:00 UTC · 7h agoAI industry 2 sources

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling

Study shows LLM test-time scaling cost depends on generation schedule: eight serial A100 calls use ~4.7x more GPU energy than one batched call.

The paper shows that candidate count N alone poorly describes the systems cost of sampling-based test-time scaling. Fixing N=8 with Phi-3-mini and Qwen2.5-1.5B on 500 GSM8K prompts, going from 1 to 8 candidates improved accuracy by 8.4 and 18.4 percentage points respectively, but eight serial generation calls on A100 GPUs consumed 4.64-4.86x more gross GPU-device energy and had 5.77-6.12x higher P95 latency than one batched 8-candidate call. The authors argue evaluations should report generation schedules and GPU-level metrics, and recommend fewer, larger batches when candidates are independent and memory allows.

Hugging Face daily papersSep 15, 20:00 UTC · 16d agoAI research

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Productive, Durable, Fungible: How NVIDIA AI Factories Maximize Return on Investment

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling