Disaggregated AI Inference

7/22/2026

This post extends the concept of disaggregated AI inference by focusing on its application to cybersecurity. It details how faster inference, particularly enabled by Cerebras's architecture, allows for more sophisticated AI-driven security operations and the protection of AI applications. The post introduces a tiered architecture for security analysis (fast initial classification, escalated deeper reasoning) and emphasizes how reduced latency transforms security workflows by enabling more context, validation, and reasoning within operational time constraints. It also highlights specific use cases and partnerships in the cybersecurity domain.

7/10/2026

This post details the collaboration with Upstage to bring ultra-fast AI inference to South Korea, showcasing Upstage's Solar 31B model achieving up to 2,000 tokens per second on the Cerebras Wafer-Scale Engine. It highlights the benefits of fast inference for real-time AI applications, such as improved search, more natural voice agents, and faster business workflows. The post emphasizes the ease of integration for developers through OpenAI-compatible APIs and the production-scale inference capabilities offered by Cerebras. It also provides a specific example of a deep research query executed by Solar 31b, contrasting its speed with another model.

7/8/2026

This post demonstrates the practical application of fast multimodal AI inference using Gemma 4 on Cerebras hardware. It showcases three specific use cases: document analysis (achieving a 17x speedup over GPUs), image search, and a rental car damage scout application that processes video frames. The post details the performance metrics for these applications, highlighting the latency benefits of Cerebras for multimodal tasks. It also provides seven practical tips for developers to build fast multimodal applications, focusing on prompt engineering, model configuration, history management, and tool orchestration. The core contribution is the empirical evidence of low-latency multimodal inference and actionable guidance for its implementation.

6/29/2026

Introduces Gemma 4 31B, a multimodal model, to the Cerebras Inference platform, achieving over 1,800 tokens per second. This represents a significant advancement in multimodal AI inference speed and latency, enabling real-time visual and agentic workflows. The post highlights the model's capabilities in image understanding combined with wafer-scale speed, unlocking new product experiences like screenshot-to-insight, long-context summarization, and screenshot-to-patch generation. It also positions Gemma 4 as a reference medium-size model on Cerebras, comparable in intelligence to Claude Haiku but significantly faster.

5/26/2026

This post extends the concept of disaggregated AI inference by framing it within the context of "Sovereign AI" and the "Cerebras for Nations" initiative. It highlights how Cerebras's high-performance AI infrastructure enables nations to build, deploy, and govern AI on their own terms, emphasizing speed, scale, and operational ease. The post details specific national use cases in the US, UAE, and India, showcasing how Cerebras's technology supports scientific research, local language model development and deployment, and national-scale AI compute infrastructure. It reiterates the performance advantages of Cerebras systems for both training and inference, positioning them as key enablers for sovereign AI capabilities.

5/19/2026

This post details the successful deployment and performance benchmarking of the Kimi K2.6 trillion-parameter model on Cerebras hardware. It highlights achieving 981 output tokens per second, a 6.7x improvement over the next fastest GPU cloud and 23x over the median inference provider. The post explains the technical approach, including distributing weights across multiple wafers, streaming activations, and utilizing on-wafer all-to-all communication with over 200x the bandwidth of NVLink. It also mentions performing computation at 16-bit floating point while storing weights in 4-bit, combined with custom kernels and speculative decoding, to achieve this record-breaking performance for trillion-parameter MoE models.

5/6/2026

Introduces Multi-LoRA support for Cerebras Inference, allowing multiple LoRA adapters to be used with a single base model for specialized inference. This enables per-request adapter switching, enhancing the flexibility and efficiency of AI applications like coding assistants by allowing specialization for different languages, tasks, and customers.

3/26/2026

This post introduces and explains the concept of disaggregated AI inference, detailing the separation of prefill and decode phases. It highlights the technical advantages of this approach, particularly the benefits of heterogeneous disaggregation by pairing compute-optimized systems (AWS Trainium) with memory-bandwidth-optimized systems (Cerebras CS-3). The post quantifies the performance gains expected in terms of token throughput and tail latency, and discusses the underlying hardware capabilities of the Cerebras WSE-3 chip, emphasizing its on-chip SRAM and memory bandwidth as key differentiators for the decode phase.

3/20/2026

This post highlights the increasing importance of inference speed in the AI race, driven by its impact on model iteration and software development velocity. It provides examples of major AI labs prioritizing speed, such as Google's Gemini 3 Flash, Anthropic's faster Claude Opus 4.6, and OpenAI's partnership with Cerebras for GPT-5.3-Codex-Spark. The post explains how fast inference enables recursive self-improvement of AI models and accelerates product development cycles, citing Anthropic's pricing strategy and a hypothetical scenario of two companies developing an AI-powered CRM. It connects this trend to the broader digital economy's historical focus on speed and positions high-speed inference as critical infrastructure for the AI era.