inference
Purpose-built AI inference architecture: Reengineering compute design
Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed and energy efficiency take a back seat to sheer computational scale. Training systems can afford to be slow and power-hungry; if it takes an extra day or…
GitHub – YuminosukeSato/pyproc: Call Python from Go without CGO or microservices – Unix domain socket based IPC for ML inference and data processin
Run Python like a local function from Go — no CGO, no microservices. 🎯 Purpose & Problem Solved Go excels at building high-performance web services, but sometimes you need Python: Machine Learning Models: Your models are trained in PyTorch/TensorFlow Data Science Libraries: You need pandas, numpy, scikit-learn Legacy Code: Existing Python code that’s too costly…
The next AI frontier: AI inference for less than $0.002 per query
Inference is rapidly emerging as the next major frontier in artificial intelligence (AI). Historically, the AI development and deployment focus has been overwhelmingly on training with approximately 80% of compute resources dedicated to it and only 20% to inference. That balance is shifting fast. Within the next two years, the ratio is expected to reverse…
Positron believes it has found the secret to take on Nvidia in AI inference chips — here’s how it could benefit enterprises
Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now As demand for large-scale AI deployment skyrockets, the lesser-known, private chip startup Positron is positioning itself as a direct challenger to market leader Nvidia by offering dedicated, energy-efficient, memory-optimized…
Enhancing AI Inference: Advanced Techniques and Best Practices
When it comes to real-time AI-driven applications like self-driving cars or healthcare monitoring, even an extra second to process an input could have serious consequences. Real-time AI applications require reliable GPUs and processing power, which has been very expensive and cost-prohibitive for many applications – until now. By adopting an optimizing inference process, businesses can…
LLMs Can Now Reason in Parallel: UC Berkeley and UCSF Researchers Introduce Adaptive Parallel Reasoning to Scale Inference Efficiently Without Exceeding Context Windows
Large language models (LLMs) have made significant strides in reasoning capabilities, exemplified by breakthrough systems like OpenAI o1 and DeepSeekR1, which utilize test-time compute for search and reinforcement learning to optimize performance. Despite this progress, current methodologies face critical challenges that impede their effectiveness. Serialized chain-of-thought approaches generate excessively long output sequences, increasing latency and…
DeepSeek jolts AI industry: Why AI’s next leap may not come from more data, but more compute at inference
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The AI landscape continues to evolve at a rapid pace, with recent developments challenging established paradigms. Early in 2025, Chinese AI lab DeepSeek unveiled a new model that sent shockwaves through the AI industry and resulted…