- From Prompt to a Shipped Hugging Face Model
- 3 awesome Paramount+ movies new to watch this week (May 4
- Top Search and Fetch APIs for Building AI Agents in 2026: Tools, Tradeoffs, and Free Tiers
- Forget the Pixel 10a — Mint Mobile will give you a base Google Pixel 10 AND a year of Unlimited for only $480
- ‘Nature’ Retracts Paper on the Benefits of ChatGPT in Education
- Is the Motorola Razr 2026 waterproof?
- Testing SQL Like a Software Engineer: Unit Testing, CI/CD, and Data Quality Automation
- WhatsApp’s ‘Avatars’ are being removed from the Android app
Browsing: inference
IBM Releases Two Granite Speech 4.1 2B Models: Autoregressive ASR with Translation and Non-Autoregressive Editing for Fast Inference
IBM released two new open speech recognition models— Granite Speech 4.1 2B and Granite Speech 4.1 2B-NAR — and they make a compelling case for what…
Top 10 KV Cache Compression Techniques for LLM Inference: Reducing Memory Overhead Across Eviction, Quantization, and Low-Rank Methods
As large language models scale to longer context windows and serve more concurrent users, the key-value (KV) cache has emerged as a primary memory bottleneck in…
Organizations are racing to deploy generative AI models into production to power intelligent assistants, code generation tools, content engines, and customer-facing applications. But deploying these models…
A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence
class QwenChat: def __init__(self, model, processor, system=None, tools=None): self.model, self.processor = model, processor self.tokenizer = processor.tokenizer self.history: list[dict] = [] if system: self.history.append({“role”: “system”, “content”: system})…
A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning
import subprocess, sys, os, shutil, glob def pip_install(args): subprocess.run([sys.executable, “-m”, “pip”, “install”, “-q”, *args], check=True) pip_install([“huggingface_hub>=0.26,<1.0”]) pip_install([ “-U”, “transformers>=4.49,<4.57”, “accelerate>=0.33.0”, “bitsandbytes>=0.43.0”, “peft>=0.11.0”, “datasets>=2.20.0,<3.0”, “sentence-transformers>=3.0.0,<4.0”, “faiss-cpu”, ])…
As the demand for generative AI continues to grow, developers and enterprises seek more flexible, cost-effective, and powerful accelerators to meet their needs. Today, we are…
A End-to-End Coding Guide to Running OpenAI GPT-OSS Open-Weight Models with Advanced Inference Workflows
In this tutorial, we explore how to run OpenAI’s open-weight GPT-OSS models in Google Colab with a strong focus on their technical behavior, deployment requirements, and…
Cost-efficient custom text-to-SQL using Amazon Nova Micro and Amazon Bedrock on-demand inference
Text-to-SQL generation remains a persistent challenge in enterprise AI applications, particularly when working with custom SQL dialects or domain-specific database schemas. While foundation models (FMs) demonstrate strong…
Practical benchmarks showing faster inter-token latency when deploying Qwen3 models with vLLM, Kubernetes, and AWS AI Chips. Speculative decoding on AWS Trainium can accelerate token generation…
Deploying and scaling foundation models for generative AI inference presents challenges for organizations. Teams often struggle with complex infrastructure setup, unpredictable traffic patterns that lead to…
