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In this tutorial, we work through an end-to-end workflow for Qualcomm AI Hub Models. We start by setting up the required package, discovering the available model collection, and loading MobileNet-V2 for local PyTorch inference. We also handle an important input-shape issue by converting NHWC image tensors into the NCHW format expected by the model. From […]
Google DeepMind released Quantization-Aware Training (QAT) checkpoints for the Gemma 4 family. The release targets local deployment on edge devices and consumer GPUs. It follows the Gemma 4 launch in April and a 12B model two days earlier. We compared the available Gemma 4 edge-model formats using only published numbers. The goal was simple. Show
In production inference deployments, demand fluctuates over time, requiring inference replicas to scale elastically. Cold-starting inference workloads on Kubernetes can take several minutes. During that time, GPUs are allocated but idle, generating no tokens and serving no requests. ‘Cold start’ means the full sequence a model server must complete before serving any request: pulling the
Perplexity AI announced what it calls the first hybrid local-server inference orchestrator at Computex 2026. The system is designed to automatically route AI tasks between a user’s local device and cloud-based frontier models without requiring the user to decide in advance. The feature is expected come to Perplexity Computer in July 2026. What is Hybrid
In this tutorial, we set up Microsoft Fara in Google Colab and run a browser-use workflow from start to finish. We begin by cloning the repository, installing the package, preparing Playwright, and verifying that the installed Fara files work even when the package layout changes. Instead of immediately relying on a heavy Fara-7B deployment, we
In this tutorial, we work with the amphora/ResearchMath-14k dataset, a collection of research-level mathematics problems mined from arXiv. We load the dataset, inspect its structure, and explore how the problems are distributed across mathematical fields and open-status categories. We then move beyond basic analysis by extracting field-specific keywords, generating semantic embeddings, visualizing the problem landscape,
NVIDIA has released Nemotron 3 Ultra, the largest model in its Nemotron 3 family. It targets a specific problem: long-running agents that plan, call tools, and reason across many turns. As agents run longer, token counts grow and inference cost climbs. Nemotron 3 Ultra is designed to keep accuracy high while making that inference faster
Miso Labs has released MisoTTS, an open-weights 8-billion-parameter text-to-speech model. It generates expressive speech from both text and audio context. The model uses residual vector quantization (RVQ) to widen its sonic range. This avoids scaling a single flat vocabulary while keeping parameter count fixed. What is MisoTTS MisoTTS is an 8B-parameter text-to-dialogue RVQ Transformer. It
Miso Labs Releases MisoTTS: An 8B Emotive Text-to-Speech Model with Open Weights Read More »
Researchers at Stanford University and Lambda Labs, have published the research paper for OpenJarvis, an open-source framework that runs inference, agents, memory, and learning entirely on-device. The open-weight models configured through OpenJarvis land within 3.2 percentage points of the best cloud model on average, at roughly 800× lower marginal API cost per query and roughly
In this tutorial, we build a document-intelligence workflow with iii. We begin by installing the iii engine and Python SDK, then start the engine as a background process and connect a Python worker to it. After the setup, we register separate functions for text normalization, tokenization, sentiment analysis, keyword extraction, reporting, and heartbeat tracking. We
