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.

Fireworks AISince 2023-09

Fireworks AI is an enterprise AI inference and model serving platform built to run open-weights LLMs, vision models, and multimodal architectures with lightning-fast speeds and lowest cost. Created by former Meta AI and PyTorch infrastructure engineers, Fireworks powers millions of daily AI requests with sub-100ms time-to-first-token (TTFT) and high token throughput. Fireworks allows developers to seamlessly deploy, fine-tune, and serve models like Llama 3.1/3.3, DeepSeek-R1/V3, Mixtral, Qwen 2.5, and Flux.1 with zero cold starts. It uniquely supports instant LoRA fine-tuning switching on shared GPU infrastructure, allowing thousands of custom fine-tuned adapters to run without paying for dedicated hardware.

The Fireworks AI engine utilizes proprietary GPU compilation optimizations, speculative decoding, dynamic kernel fusing, and custom tensor-parallel kernels to maximize memory bandwidth and FLOPS efficiency on NVIDIA H100 and B200 clusters. Fireworks provides a fully OpenAI-compatible REST and streaming API alongside native function calling, JSON schema guarantees, and multimodal image input. Its FireAttention technology drastically cuts KV-cache memory overhead, enabling massive concurrency and context lengths up to 128k tokens while maintaining deterministic latency SLAs.