Cerebras Inference

Cerebras InferenceSince 2024-08

Cerebras Inference is the world's fastest AI inference platform, powered by the revolutionary Cerebras CS-3 Wafer-Scale Engine (WSE-3). Delivering unprecedented speeds exceeding 2,100 tokens per second on Llama 3.1 8B and over 450 tokens per second on Llama 3.1 70B, Cerebras runs AI inference up to 20x faster than traditional NVIDIA GPU clusters. By replacing traditional GPU memory bandwidth bottlenecks with 44 Gigabytes of on-chip SRAM across a monolithic silicon wafer, Cerebras achieves instantaneous response times that transform conversational AI, real-time code synthesis, and multi-step agentic reflection loops into fluid, zero-latency interactions.

Traditional GPUs are limited by external HBM/DRAM bandwidth, forcing token generation to stall while weights are retrieved across PCIe buses. The Cerebras WSE-3 features 900,000 AI-optimized compute cores and 21 Petabytes/sec of memory bandwidth directly on a single silicon wafer. Cerebras Inference provides a 100% OpenAI-compatible API, allowing developers to switch their application endpoints with zero code modifications. It supports streaming completions, tool calling, JSON structured schemas, and massive token context lengths with guaranteed instantaneous time-to-first-token.

QdrantSince 2025-03

Qdrant is an open-source, high-performance vector database and similarity search engine engineered in Rust for production AI systems, semantic search engines, and Retrieval-Augmented Generation (RAG) pipelines. It provides lightning-fast nearest-neighbor search with rich payload filtering and custom distance metrics. Unlike traditional databases adapted for vectors, Qdrant was designed from day one to handle high-dimensional neural embeddings at scale. Its Rust engine provides memory-efficient vector quantization (scalar, product, and binary), allowing engineering teams to search billions of vectors on cost-effective cloud hardware.

Qdrant features advanced hybrid search capabilities, combining dense vector embeddings with sparse BM25 keyword vectors and lexical filters in a single query execution plan. It includes native multi-tenant payload partitioning, dynamic indexing, and zero-downtime collection snapshots. With client SDKs for Python, TypeScript, Go, Rust, and Java, Qdrant powers mission-critical search infrastructures for thousands of modern AI applications.