High-Performance LLM Inference with Cerebras Wafer-Scale Engine: Deploy Production AI with OpenAI-Compatible API, Ultra-Fast Token Generation, and Cos

Name: High-Performance LLM Inference with Cerebras Wafer-Scale Engine: Deploy Production AI with OpenAI-Compatible API, Ultra-Fast Token Generation, and Cos
Brand: Independently Published
SKU: 9798241828620
Price: 4075.0 INR
Availability: OutOfStock

by Lina Takashi

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ISBN13: 9798241828620
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: December 29, 2025
Pages: 386
Original Price: USD 38.99
Language: English
Edition: N/A
Item Weight: 667 grams
BISAC Subject(s): Data Science / Neural Networks

About The Book

Ship fast, reliable LLM products on wafer scale hardware without guessing your way through performance, cost, and safety.

Many teams can call an LLM API, but far fewer can keep latency, errors, and cost under control once real users, real traffic, and real constraints show up. GPU based stacks hit memory, interconnect, and scheduling bottlenecks just when you need predictable performance the most.

High-Performance LLM Inference with Cerebras Wafer-Scale Engine gives you a practical, end to end playbook for running production workloads on Cerebras Inference Cloud with an OpenAI compatible surface. You learn how to measure what matters, migrate safely, and operate a high throughput inference gateway that your team can actually support.

Understand TTFT, throughput, tail latency, concurrency, and how to build a minimal benchmark harness with repeatable test matrices.
See why wafer scale changes inference bottlenecks compared to multi GPU sharding, and where it truly improves latency and stability.
Work with Cerebras Inference Cloud service models, tenancy, quotas, authentication, secret management, and robust request lifecycles.
Migrate from existing OpenAI based clients using a compatible API, parameter mapping, response normalization, version pinning, and feature flags.
Select and route between models using context limits, latency budgets, quality tiers, fallbacks, canary policies, and streaming UX patterns.
Design prompt caching, rate limit aware high concurrency clients, and token based cost models that keep spend predictable at scale.
Build reliable structured JSON outputs, schema validation and repair loops, safe tool calling with firewalls and auditing, and product level reasoning controls.
Set up observability, logs, metrics, traces, key management, incident playbooks, and a reference inference gateway with a clear operational runbook.

This is a code heavy guide with concrete Python clients, benchmark runners, analysis scripts, gateway components, and configuration examples that you can adapt directly into your own stack.

Grab your copy today and give your team a clear path to high performance LLM inference on wafer scale systems.

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