Large language model inference
AssessTechniques
The process of running trained LLMs to generate outputs from prompts.
Why it's here
Placed in Assess: 4 article(s) of evidence from 5 source(s), led by research-stage coverage, with 2 in the last 30 days. Confidence 69%.
Evidence (4)
- 4InfoQ·8/11/2026researchHow to Cut LLM Inference Costs for High-Volume Batch Workloads
Meryem Arik outlines strategies for building low-cost LLM inference systems for high-volume, non-real-time workloads. The talk emphasizes trade-offs across hardware, inference runtimes, speculative decoding, and queue reordering to achieve major cost reductions.
- 6InfoQ·7/27/2026framework_updateNetflix Explains Its In-House LLM Serving Platform
Netflix outlined the production lessons behind its internal LLM inference serving platform, focusing on how it supports models of different sizes and hardware requirements. The company also highlighted the need to adapt quickly as inference engines continue to evolve, with Triton and vLLM among the technologies involved.
- 8Hacker News·6/27/2026researchDSpark: speculative decoding for faster LLM inference
DSpark is a research paper describing a speculative decoding approach aimed at reducing latency and improving throughput for large language model inference. It proposes a method that can accelerate generation while preserving output quality, making it relevant for serving and deployment efficiency.
- 8OpenAI Blog·6/24/2026product_launchOpenAI and Broadcom unveil Jalapeño inference chip
OpenAI and Broadcom have announced Jalapeño, a new chip built specifically for large language model inference in data centers. The companies said it is the first generation of a longer-term effort to refine the chip over time and deploy it at large-scale data centers.