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Optimizing LLM Inference: From Knowledge Distillation to Speculative Decoding
AI/ML14 min read

Optimizing LLM Inference: From Knowledge Distillation to Speculative Decoding

Introduction Part 2: Knowledge Distillation, KV Caching, and Speculative Decoding In the first part, quantization and pruning were discussed as techniques to optimize Large Lang...

13 August 2026Read
Comparing Vector Search Solutions: Weaviate, OpenSearch, and pgvector
Databases6 min read

Comparing Vector Search Solutions: Weaviate, OpenSearch, and pgvector

Introduction When your application requires search capabilities, you might consider tools such as OpenSearch for full text search or PostgreSQL with the pgvector extension for a...

13 August 2026Read
Understanding the Shift to Mixture of Experts Models and Its Impact on Inference Costs
AI/ML4 min read

Understanding the Shift to Mixture of Experts Models and Its Impact on Inference Costs

Overview Key Points By 2025 2026, most major open weight large language models (LLMs) have adopted the Mixture of Experts (MoE) architecture, including models like Llama 4, Deep...

13 August 2026Read
Choosing the Right Hosting for Your Sub-10B Parameter Open-Source Model
AI/ML7 min read

Choosing the Right Hosting for Your Sub-10B Parameter Open-Source Model

Introduction: Understanding the Real Hosting Challenge When it comes to hosting models with fewer than 10 billion parameters, the challenge isn't finding a service that can tech...

13 August 2026Read
Creating a Medical Report Analysis Tool with Python and Dedicated Inference
AI/ML14 min read

Creating a Medical Report Analysis Tool with Python and Dedicated Inference

Introduction Medical reports are typically crafted for healthcare professionals, not patients. Values like or are significant only if you understand what these metrics represent...

13 August 2026Read
Understanding Key Metrics for Serverless LLM Inference
AI/ML8 min read

Understanding Key Metrics for Serverless LLM Inference

Introduction When assessing serverless large language model (LLM) inference models and their providers, the focus often narrows down to one metric: median tokens per second. Whi...

13 August 2026Read
Optimizing AI Inference with MoE Routing for Efficiency and Cost Savings
AI/ML6 min read

Optimizing AI Inference with MoE Routing for Efficiency and Cost Savings

Introduction: The Cost of Using Uniform AI Models Consider a request that just reached your API: This is a simple syntax check, and the answer is no, there are no issues. Such a...

13 August 2026Read
Optimizing API Costs with a Multi-Model Inference Router
AI/ML11 min read

Optimizing API Costs with a Multi-Model Inference Router

Introduction An inference router acts as an intermediary layer that connects your application to the model serving layer. Instead of routing every API call to a single endpoint,...

13 August 2026Read
Streamlining AI Agent Functionality: Understanding Server-Side Tools and Their Benefits
AI/ML9 min read

Streamlining AI Agent Functionality: Understanding Server-Side Tools and Their Benefits

Every AI agent faces a common challenge: while the model can process information, it requires external tools to execute actions. Typically, the execution of these tools—fetching...

13 August 2026Read
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