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Fast Local LLM Inference Benchmarks and Deployment Tips
Community benchmarks and infra recommendations for local models.
FlashQwen: New CUDA Inference Engine for Qwen3
FlashQwen: Revolutionizing CUDA Inference with Qwen3 In the ever evolving field of machine learning, the efficiency of inference engines plays a pivotal role. I…
AirLLM 70B Runs on 4GB GPU: AI Breakthrough
AirLLM 70B inference with single 4GB GPU
Groq Aims to Raise $650M for AI Inference Focus After Nvidia Deal
Chipmaker Groq is looking to raise $650 million in internal funding as it pivots from hardware to focus more on AI inference, the process of refining the way AI models respond to prompted requests, per Axios.
Tiny-vLLM: High-Performance LLM Inference in C++ and CUDA
Tiny vLLM: Revolutionizing High Performance LLM Inference Tiny vLLM stands at the forefront of high performance inference for large language models (LLMs), desi…
NeuroFlow Accelerates Vision Transformers in PyTorch 55.8x
NeuroFlow Accelerates Vision Transformers in PyTorch by 55.8x In the realm of machine learning, the efficiency and speed of transforming vision models are param…
Llama.cpp: Efficient LLM Inference in C/C++ on GitHub
LLM inference in C/C++
AI Infrastructure Startup Secures Funding for Scalable Inference Stack
News about venture investment in scalable AI inference infrastructure.
Track Real-Time GPU & LLM Pricing Across Cloud Providers
Deploybase is a dashboard for tracking real-time GPU and LLM pricing across cloud and inference providers. You can view performance stats and pricing history, compare side by side, and bookmark to track any changes. https://deploybase.ai
Nvidia Exec: AI Currently More Expensive Than Human Workers
Nvidia’s vice president of applied deep learning, Bryan Catanzaro, recently stated that for his team, “the cost of compute is far beyond the costs of the employees,” highlighting that AI is currently more expensive than human workers. This challenges the narrative that widespread tech layoffs (including Meta’s planned cut of \~8,000 jobs and Microsoft’s voluntary buyouts) signal an imminent replacement of humans by AI. An MIT study from 2024 supports this, finding that AI automation is economically viable in only 23% of roles where vision is central, and cheaper for humans in the remaining 77%. Despite heavy AI investment—Big Tech has announced $740 billion in capital expenditures so far this year, a 69% increase from 2025—there is still no clear evidence of broad productivity gains or job displacement from AI. AI spending is driving up costs, with some executives like Uber’s CTO saying their budgets have already been “blown away.” Experts describe the situation as a short-term mismatch: high hardware, energy, and inference costs make AI less efficient than humans right now, though future improvements in infrastructure, model efficiency, and pricing models could tip the balance toward greater economic viability in the coming years.