nvidia-smi Reports 97% Utilization While the GPU Sits Idle
TL;DR A GPU shows 97% utilization in nvidia-smi , but training throughput is a fraction of what benchmarks promise. The GPU is not computing; it is wa…
Latest Web news from Tech News
All topics
AI
agents
ai
api
architecture
automation
aws
beginners
career
claude
database
devchallenge
devops
javascript
learning
linux
llm
machinelearning
mcp
opensource
performance
productivity
programming
python
react
security
showdev
tutorial
typescript
webdev
Невозможно быть вне политики с Airflow Cluster Policies
Привет, Хабр! Я Миша Онянов, Python-разработчик и платформенный инженер в крупнейшем проекте MAGNIT TECH – F&R. Из статьи вы узнаете, как с помощь…
I Processed 2.4 Billion Tokens Across 52 AI Models for $0.52. Here's the Full Breakdown.
I run a production multi-agent AI system on a single M1 Mac in Jamaica. 6 autonomous agents. 26 cron workflows. 5-layer persistent memory. All contain…
Quantization formats compared: GGUF vs GPTQ vs AWQ vs NF4
Quantization formats compared: GGUF vs GPTQ vs AWQ vs NF4 You just finished fine-tuning a 7B parameter model. The raw FP16 weights are 14 GB. Your tar…
I Built a Production RAG System on My M1 Mac for $0
I Built a Production RAG System on My M1 Mac for $0 Most RAG tutorials stop at "it answers questions." But answering questions is table stakes. The re…
Per-project LLM cost attribution with OTel spans: the wiring
TL;DR. If your LLM bill is one line item on a cloud invoice, you cannot answer "which team spent that." We fixed this by tagging every gateway span wi…
Our event-camera detector lost 6 mAP to a badly chosen accumulation window
TL;DR: We spent three weeks chasing a 6 mAP regression in an event-camera object detector. The model was fine. The bug was the accumulation window we …
I Built a Complete AI Infrastructure Stack from Scratch — Here's What I Learned
I Built a Complete AI Infrastructure Stack from Scratch — Here's What I Learned Most AI projects start at the top of the stack. You grab an LLM API, w…
QAT vs PTQ on our edge vision model: 6 months of A/B data
TL;DR: We ran post-training quantisation (PTQ) and quantisation-aware training (QAT) side by side on the same defect-classification model deployed on …
LLM-as-judge variance broke our DPO training signal for 3 weeks
TL;DR: Our DPO pipeline used a single LLM as the preference judge. Training reward climbed every run. Production accuracy fell 4 points. The judge was…
The bf16 grad accumulator that killed our SDXL LoRA training
TL;DR: Our SDXL LoRA fine-tune for a Photoroom product photography model trained for six days while silently corrupting its adapter weights. The cause…
Prefix caching in vLLM under multi-tenant agent traffic
TL;DR: We turned on vLLM's prefix cache for our agent workloads at Nexus Labs and watched TTFT drop from 480ms to 110ms on one tenant and stay exactly…
Part 2: Enterprise Decision Intelligence Architecture: AI Governance, Threshold Policy Engines, and Operational AI Systems
Part 1 showed how to evaluate binary classification thresholds in Python. This part asks the harder enterprise question: What happens when that thresh…
I built a token-level debugger for comparing two LLMs
Same prompt, two models, different outputs. No tooling was actually showing me where they diverged. Built tokenflame that gives entropy heatmaps, toke…
How to Detect GPU Waste in a Kubernetes Cluster
GPU waste in Kubernetes does not announce itself. Your cluster shows healthy utilization. Your dashboards are green. But 20–40% of your GPU capacity i…
Why 91% of AI Agents Fail in Production (And What the 9% Do Differently)
Everyone is building AI agents right now. Autonomous systems that reason, plan, and act without humans in the loop. Agents that write code, manage wor…
llm-nano-vm v0.8.0 — deterministic FSM runtime for LLM pipelines, now with output validation and per-step timeouts
PyPI: pip install llm-nano-vm GitHub: http://github.com/Ale007XD/nano_vm MCP gateway: http://github.com/Ale007XD/nano-vm-mcp I've been building a dete…
Stop paying for idle GPUs in your CI: batching LLM eval jobs
TL;DR: Running LLM evaluations on every PR will burn your GPU budget faster than you can blink. We cut our eval spend by about 60% by batching jobs in…
Why your diffusion model is slow at batch size 1 (and what actually helps)
TL;DR: Single-image diffusion inference is bottlenecked by kernel launch overhead and attention memory traffic, not raw FLOPs. torch.compile with mode…
When AI Meets Reality: Why “Hello World” Isn’t Enough for LLM Systems
Most AI tutorials stop at “Hello World.” You wire up a model, send a prompt, get a response, and feel like you’ve built something. But the moment you …
What GenAI Actually Costs in Production
The first number anyone quotes when asked what generative AI costs is a per-token figure. It is a comfortable number — small, unambiguous, available o…
The Missing Engineering Stack for Production AI Agents
The "build an agent in 5 minutes" tutorials get you to a demo. They don't get you to production. Here's the field guide for the four primitives that d…
Инженерный подход к MLOps: как принципы расчётной механики ложатся в архитектуру AutoML
«Если что-то может пойти не так, это обязательно случится» . Мы не пытаемся предотвратить отказ, мы проектируем систему так, чтобы отказ одного элемен…
How we catch silent NPU fallback on Snapdragon in CI (and why your eval set won't)
TL;DR — ONNX Runtime's QNN execution provider will quietly route unsupported ops to the CPU instead of the Hexagon NPU. Your accuracy is fine. Your ev…
Beyond Monitoring: Building AI-Powered Predictive Observability for Retail Data Pipelines published
Three numbers before we start: Average detection time with traditional monitoring: 4.2 hours Average detection time with predictive observability: 11 …
Когда автоматизация становится умнее: как трансформеры изменили AutoDL в Альфа-Банке
Всем привет! С вами Артемий Лямин ( @lyaminartemiy ) и Иван Тренёв ( @123-39 ). Мы работаем специалистами по разработке нейронных сетей в команде авто…