Schedules requests at the iteration level instead of waiting for a full static batch, so finished sequences are evicted and new ones join immediately. Keeps the GPU saturated and delivers large throughput gains under concurrent load.

FlashAttention

IO-aware exact attention algorithm that reduces memory reads/writes by tiling and recomputation. Provides 2-4x speedup and enables longer context lengths without approximation.

KV Cache Offloading

Moves KV cache blocks from GPU memory to CPU RAM or local storage when they are not actively needed, then loads them back on demand. Frees up scarce GPU memory to support larger batches and longer contexts.

PagedAttention

Virtual memory-inspired KV cache management that stores cache in non-contiguous blocks to eliminate fragmentation. Enables near-zero memory waste, larger batches, and higher throughput, and is the foundation of vLLM.

Parallelism

Distributes a model and its computation across multiple GPUs and nodes. Data, tensor, pipeline, expert, and hybrid strategies trade off memory, communication, and throughput to serve models too large for a single device.

Prefill-Decode Disaggregation

Splits the compute-bound prefill phase and the memory-bound decode phase onto separate GPU pools so each can be scaled and optimized independently. Reduces interference between the two phases and improves both latency and throughput.

Prompt Caching

Reuses the KV cache of shared prompt prefixes across requests so common system prompts and few-shot examples are computed only once. Dramatically cuts time-to-first-token for workloads with repeated context.

Quantization

Reduces the numerical precision of weights and activations (for example, FP16 to FP8 or 4-bit) to shrink memory footprint and sometimes speed up inference.

Speculative Decoding

Uses a smaller draft model to generate candidate tokens that the larger model verifies in parallel. Achieves 2-3x faster decoding without any quality loss.

Continuous Batching

FlashAttention

IO-aware exact attention algorithm that reduces memory reads/writes by tiling and recomputation. Provides 2-4x speedup and enables longer context lengths without approximation.

KV Cache Offloading

PagedAttention

Parallelism

Prefill-Decode Disaggregation

Prompt Caching

Quantization

Reduces the numerical precision of weights and activations (for example, FP16 to FP8 or 4-bit) to shrink memory footprint and sometimes speed up inference.

Speculative Decoding

Uses a smaller draft model to generate candidate tokens that the larger model verifies in parallel. Achieves 2-3x faster decoding without any quality loss.

Why Optimizations Matter

Running LLMs at scale requires clever engineering. From quantization that shrinks model weights to attention mechanisms that reduce memory usage, these techniques make it possible to serve powerful models on commodity hardware.

4-8x

Memory reduction with quantization

2-4x

Throughput gain with FlashAttention

23x+

Throughput with continuous batching

2-3x

Faster inference with speculative decoding

View all optimizations →

Agents

Hottest Agentic Frameworks

View all

Agent Development Kit

Google

21.2k

Open source toolkit from Google for building, evaluating, and deploying AI agents. Model-agnostic and optimized for Gemini, with multi-agent orchestration, tools, and tight Vertex AI integration.

multi-agentGeminiGooglePython

Agno

41.8k

High-performance, full-stack framework for building multi-agent systems with memory, knowledge, and reasoning. Model-agnostic with built-in tools, structured outputs, and a path to production (formerly Phidata).

multi-agentmemorymodel-agnosticPython

AutoGen

Microsoft

60.6k

Multi-agent conversation framework enabling agents to chat with each other to solve tasks. Supports customizable agents, human participation, and diverse conversation patterns.

conversationalmulti-agentMicrosoftPython

CrewAI

57.4k

Role-based multi-agent framework where you define agents with specific roles, goals, and backstories. Agents collaborate to complete complex tasks through delegation and tool use.

role-basedmulti-agentcollaborationPython

LangGraph

LangChain

40.1k

Framework for building stateful, multi-agent applications as graphs. Supports cycles, persistence, human-in-the-loop, and streaming. The agent orchestration layer of the LangChain ecosystem.

graph-basedstatefulmulti-agentstreaming

LlamaIndex

51.8k

Data framework for building LLM and agent applications over your own data. Provides data connectors, indexing, retrieval, and agent workflows for production RAG and knowledge assistants.

RAGdata-frameworkworkflowsPython

OpenAI Agents SDK

OpenAI

28.8k

Lightweight Python SDK for building agentic AI apps. Features agent handoffs, guardrails, tracing, and tool integration with a minimal abstraction layer over the OpenAI API.

handoffsguardrailstracingPython

Pydantic AI

Pydantic

19.4k

Agent framework built on Pydantic for type-safe AI applications. Structured outputs, dependency injection, and model-agnostic design from the team behind Pydantic and FastAPI.

type-safestructured-outputmodel-agnosticPython

smolagents

Hugging Face

28.9k

Minimalist agent library focused on code agents that write and execute Python. Simple API with tool calling, multi-step reasoning, and tight Hugging Face Hub integration.

code-agentsminimalistHugging-FacePython

View all frameworks →

The Open Source LLM Stack

Latest Open Source LLMs

Inference Optimizations

Continuous Batching

FlashAttention

KV Cache Offloading

PagedAttention

Parallelism

Prefill-Decode Disaggregation

Prompt Caching

Quantization

Speculative Decoding

Continuous Batching

FlashAttention

KV Cache Offloading

PagedAttention

Parallelism

Prefill-Decode Disaggregation

Prompt Caching

Quantization

Speculative Decoding

Why Optimizations Matter

Hottest Agentic Frameworks

Agent Development Kit

Agno

AutoGen

CrewAI

LangGraph

LlamaIndex

OpenAI Agents SDK

Pydantic AI

smolagents

The Open Source
LLM Stack