A developer built a complete AI infrastructure stack from scratch, including a crash-recovery storage engine, an LLM inference cache, a Kubernetes-based training orchestrator, and an async AI data pip…
Tensors enable hardware acceleration by leveraging GPUs and TPUs to perform parallel mathematical operations efficiently, making them essential for training neural networks. They also support automati…
Researchers have developed SIA, a self-improving AI system that updates both its own software scaffolding and internal model weights without human intervention, combining two previously separate appro…
Mixture of Experts (MoE) models are reshaping the economics of open-model deployments by reducing GPU inference costs and altering serving stack requirements. The shift toward MoE architectures in 202…
LLM inference costs 100x more than traditional machine learning inference because autoregressive generation requires a separate forward pass through the entire model for each output token. A Llama 3.1…
TokenSpeed, an open-source inference engine, achieved a record-breaking 580 tokens per second running the Qwen3.5-397B-A17B model on GPUs. The performance gain for agentic workloads comes from elimina…
NVIDIA, Amazon Web Services, and Strands have launched a multi-agent generative AI system that combines NVIDIA NIM for GPU-accelerated inference, Amazon Bedrock AgentCore for managed runtime and share…
KlongPy now supports a PyTorch backend that enables GPU acceleration and automatic differentiation for gradient-based computations. The torch backend outperforms NumPy by up to 8x on large arrays and …
Lambda Chief Commercial Officer Robert Brooks IV argued that computing power is becoming one of the most strategically important resources in the AI economy, with his company building supercomputers f…
Reiner Pope, CEO of AI chip startup MatX and former Google engineer, delivered a blackboard lecture explaining chip design from basic logic gates to the architectures of GPUs, TPUs, FPGAs, and the hum…
CODA, a GPU kernel abstraction that reparameterizes memory-bound Transformer operations like normalization and activations to execute as GEMM-plus-epilogue programs, keeping data on-chip to reduce glo…
Running large language model inference servers like vLLM and TGI in production requires specialized observability because they behave differently from standard web services, with key metrics like late…
Practical guide for engineers and security teams evaluating whether to run large AI models locally, in private cloud, or via secure enterprise platforms. It argues that AI performance depends not only…
Cerebras Systems has secured a 750MW compute deal with OpenAI, positioning the company for its upcoming IPO as demand for fast token generation surges. The wafer-scale chip maker's speed advantages, p…
Shepherd Model Gateway (SMG) has disaggregated all CPU-bound workloads from GPU inference in large language model serving, moving tokenization, detokenization, and parsing into a dedicated Rust gatewa…
The article provides a step-by-step guide on self-hosting a Git frontend service using Gitea on a Debian server with Nginx. It covers setting up a PostgreSQL database for Gitea, downloading and instal…