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Sustainable Compute in US Data Centers: AI Efficiency

Sustainable compute aims to deliver more computing work per unit energy in US data centers by optimizing hardware, models, and workloads, acknowledging that efficiency does not automatically reduce total energy demand if demand grows rapidly. Effective implementation combines multiple measures—hardware, software optimization, utilization, scheduling, and thermal management—to maximize useful output within grid and infrastructure limits.

[2610.25519] ATEST: Adaptive Resource-Aware Test-Time Scaling for LLM Inference

ATEST introduces a resource-aware test-time scaling framework for LLM inference, balancing the value of extra samples against live GPU energy costs. In online mode it adapts to load, achieving strong accuracy while staying within energy budgets; calibrated mode uses offline models but is less robust to load changes. Online cost learning significantly reduces GPU time and energy compared with offline calibration under evaluated workloads.

Efficiency becomes crucial as technology is integrated into operations - Tyrone's info

Translated title. Original: Effektivisering blir avgörande när tekniken integreras i verksamheter - Tyrone's info

Efficiency becomes essential as technology becomes part of operations; initial gains from new tech give way to ongoing optimization of resource use and changes in work practices, analogous to how electricity transformed society.

Muse Spark Open vs Mistral Large 4 Pricing ($1.25 vs $0.70/1M — Save 48%) | TokenMargin

The comparison says Mistral Large 4 costs less than Muse Spark Open for the listed token rates and production workload. At 10,000 daily active users, it estimates lower monthly bills both without caching and with a 75% cache hit rate.

LocalAI Cost — Buy the card or rent the tokens?

The document compares LocalAI cost models (card purchase vs token rental) and presents metrics like weekly usage, electricity cost, and working context options, along with cache misses, token counts per turn, and throughput with optional t/s overrides.

RunPod Pods vs Serverless GPU (vLLM / H100 / A40) & Batch Inference (-30%) Break-Even Calculator | TokenMargin.dev

TokenMargin.dev offers tools to count prompt tokens, audit AI usage bills from CSV or JSONL files, and compare GPU inference costs. It says file auditing runs in the browser without sending data to a server.

Gemini 2.0 Flash vs Claude 3.5 Haiku API Cost Calculator & Token Pricing (2026) | TokenMargin

The document compares production cost and caching effects for Gemini 2.0 Flash versus Claude 3.5 Haiku, showing Gemini as cheaper across input/output pricing and with cache savings at 75% hit rate; quantified costs are provided for 10k DAU scenarios and annual savings when routing to Gemini.

[2610.39621] The Real Cost of Context: Predicting Compute Time and Energy Consumption in LLM Prefill and Decode Phases

The paper analyzes how input and output token lengths affect compute time and energy use in large language models, showing linear scaling for decode and mixed linear/quadratic scaling for prefill. It proposes a mapping framework to estimate energy from compute time to inform sustainable deployment in agentic AI.

IA: it's not the "thank you" that costs, it's the conversation that drags on

Translated title. Original: IA : ce n'est pas le "merci" qui coûte, c'est la conversation qui s'allonge

Long AI conversations consume far more energy and money than a single polite remark, because history is sent back and forth with each turn. While caching can reduce some costs, longer histories impair reasoning and switching models mid-conversation exacerbates waste; effective conversation design is essential.

Llama 3.1 405B Instruct vs Llama 4 Maverick (17B-128E MoE) Pricing ($0.80 vs $0.19/1M — Save 67%) | TokenMargin

Compares per-1M-token costs for Llama 3.1 405B Instruct and Llama 4 Maverick (17B-128E MoE). Llama 4 Maverick is cheaper overall, especially with caching, reducing monthly and yearly costs under a 3:1 input-to-output workload; provides specific uncached and cached cost figures for 10k DAU scenarios.