8 LLM Inference Frameworks You Should Try in 2026
The article highlights tools like vLLM, which optimizes GPU memory with PagedAttention and supports OpenAI-compatible APIs, and SGLang, which pairs a backend runtime with a frontend language for complex prompting logic. Others include MAX (Modular’s kernel-level framework), TensorRT-LLM (NVIDIA’s optimized GPU inference), LMDeploy (quantization-focused), llama.cpp (minimalist C/C++ runtime),…
Key points
- vLLM uses PagedAttention to pack more concurrent requests onto GPUs with continuous batching
- SGLang co-designs backend and frontend for complex prompting and structured outputs
- Ollama offers minimal setup for local serving but lacks advanced KV cache management
8 LLM Inference Frameworks You Should Try in 2026
KDnuggets · 3 November 2026
Loading the full article…
This text was published by KDnuggets and written by Bala Priya C. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Research
All →- L-STAR uses visual LLMs to improve spatial transcriptomics domain detection · 1 src
- Instruction tuning refines AI models with supervised fine-tuning · 1 src
- MIT researchers report GPT-6 Astra natively reads and writes neural network weights, raising Qwen3.5-0.8B MATH-500 from 7.9% to 48.1% by editing 0.016% of weigh · 5 src
- Reddit mentions correlate strongest with AI visibility, Ahrefs study finds · 1 src
- How “reverse game theory” could keep AI agents in check · 1 src
Comments
via GitHub Discussions