Rent a GPU for Local AI Workloads (September 2026)
Four cloud GPU vendors compared for self-hosted LLM inference: real per-hour rates for RTX 4090, A100, and H100, plus where renting beats owning hardware.
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#inference
Local AI Inference Speed: What to Expect (September 2026)
Real tokens per second from local LLMs on consumer hardware: how quant and context change speed, why memory bandwidth is the limit, and what helps.
Local AI Power Draw and 24/7 Inference Cost (September 2026)
NVIDIA and Apple TDPs, the US residential cents-per-kWh average, and what 24/7 local inference actually adds to your electricity bill by card.
Local LLM Backends Compared: Ollama, LM Studio, llama.cpp, vLLM
How Ollama, LM Studio, llama.cpp, and vLLM differ on model format, OpenAI-compatible API, hardware support, and which to pick for a home server.
AI Bills Triple, Tokens Vanish: The Enterprise Cost Crackdown
Atlassian, Adobe, Amazon, and Citi are cutting access to frontier models after monthly AI spend hit $15M. Inside the enterprise token crunch.
A 330 GB On-Die DRAM AI Chip Lands as a Whitepaper
PhantaField's Sophon PFG-1 whitepaper claims ~95x Nvidia HBM4 bandwidth via monolithic 3D stacking. No silicon yet. Here's why it matters anyway.
Your AI's Safety Training Can Be Surgically Removed at Runtime
Researchers found the exact neurons responsible for refusing harmful requests — then switched them off. No retraining. No fine-tuning. Just geometry.
Google's TurboQuant Could Let You Run Bigger AI Models on Your Hardware
New compression algorithm achieves 6x memory reduction with zero accuracy loss. No retraining required. This matters for anyone running local AI.
Cloudflare Enters the Big Model Game: Workers AI Now Runs Kimi K2.5 With 256K Context
Cloudflare adds its first frontier-scale model to Workers AI, claiming 77% cost savings over proprietary alternatives with new caching features.
Open-Weight LLM Showdown: GTC Pivots to Inference, DeepSeek V4 Still MIA
Jensen Huang bets on inference chips, Ollama adds multimodal support, and DeepSeek V4 remains the most anticipated release that hasn't happened yet.
Nvidia GTC 2026: Vera Rubin, NemoClaw, and the $20 Billion Groq Bet
Jensen Huang's keynote today marks Nvidia's biggest pivot in years - from training chips to inference, from cloud to edge, and from prompts to autonomous agents
Your AI Prompts Are a Security Liability. Most Companies Haven't Noticed.
While enterprises focus on training data and model safety, inference - where AI actually processes requests - has become an overlooked security frontier with critical vulnerabilities.
The Week Local AI Grew Up: Ollama 0.17 and llama.cpp's New Home
Ollama delivers 40% faster inference while llama.cpp finds a permanent home at Hugging Face. Two developments that secure the future of running AI on your own hardware.
Google+UVA: Longer Reasoning Predicts Failure, Not Success
Google and UVA research shows longer AI reasoning traces correlate with wrong answers. The fix: measure how deeply the model thinks, not how much it writes.
Taalas HC1: The AI Chip That Bakes the Model Into Silicon
A Toronto startup is etching LLM weights directly into transistors, achieving 17,000 tokens per second. The catch: you can't change the model.
OpenAI Just Deployed Its First AI Model on Non-NVIDIA Chips
GPT-5.3-Codex-Spark runs on Cerebras' wafer-scale chips at 1,000+ tokens per second. It's OpenAI's first production break from NVIDIA - and it won't be the last.
Tiiny AI Pocket Lab: Scrutinizing Its 120B Claim
Tiiny AI says its 300-gram Pocket Lab runs 120B models locally. The design is plausible, but performance and privacy claims remain unverified.