{"product_id":"deepspeed-in-production-inference-optimization-and-model-deploy-llms-efficiently-with-optimized-serving-quantization-and-low-latency-inference-for-9798274507356","title":"Deepspeed in Production: INFERENCE OPTIMIZATION AND MODEL: Deploy LLMs efficiently with optimized serving, quantization, and low-latency inference for","description":"\u003cp\u003e • Author(s): Tara Malhotra\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Intelligence (AI) \u0026amp; Semantics\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRun large language models with predictable latency, controlled cost, and production reliability.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eShipping LLMs is an operational problem. Teams struggle with time to first token, tokens per second, GPU memory pressure, and a moving target of engines and datatypes. This book turns those issues into clear practices you can apply with DeepSpeed and the serving layers you already use.\u003c\/p\u003e\u003cp\u003eYou get a practical path from checkpoint to stable API, with configuration that fits real workloads, not toy demos. Every topic is grounded in measurable outcomes so your stack meets SLOs under mixed traffic and budget constraints.\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eplace DeepSpeed correctly in your stack and configure kernel injection, tensor parallel, and ZeRO for real services\u003c\/li\u003e\n\u003cli\u003eunderstand TTFT and throughput from prefill to decode and set metrics for p95 latency and queue time\u003c\/li\u003e\n\u003cli\u003esize and control the KV cache with paged attention, batching, and safe headroom targets\u003c\/li\u003e\n\u003cli\u003eapply quantization that holds up under load, including w8a8, awq, gptq, fp8, and fp4\u003c\/li\u003e\n\u003cli\u003euse speculative decoding with a sound drafter choice, acceptance math, and stable fallbacks\u003c\/li\u003e\n\u003cli\u003eoperate vllm, tensorrt llm on triton, and tgi with clean api surfaces and core flags\u003c\/li\u003e\n\u003cli\u003escale with ray serve and plan capacity from workload shapes and arrival patterns\u003c\/li\u003e\n\u003cli\u003etune for nvidia hopper and blackwell or amd mi300x, with attention backends and nvlink planning\u003c\/li\u003e\n\u003cli\u003erun on kubernetes with gpu operator, device plugin, mig, and topology aware placement\u003c\/li\u003e\n\u003cli\u003ewire observability with prometheus, dcgm, and opentelemetry spans, plus vllm bench, trtllm bench, and genai perf\u003c\/li\u003e\n\u003cli\u003eship safely with quotas, redaction, audit logs, go live gates, and instant rollback plans\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThis is a code heavy guide with working YAML, JSON, Shell, and Python examples that map directly to production, from gateway limits and network policies to rollout templates and exportable benchmark scripts.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGrab your copy today and build an LLM service that stays fast, measurable, and dependable.\u003c\/b\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47779096461463,"sku":"9798274507356","price":3014.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798274507356.webp?v=1778033373","url":"https:\/\/atlanticbooks.com\/products\/deepspeed-in-production-inference-optimization-and-model-deploy-llms-efficiently-with-optimized-serving-quantization-and-low-latency-inference-for-9798274507356","provider":"Atlantic Books","version":"1.0","type":"link"}