# 8 LLM Inference Frameworks You Should Try in 2026

Digest AI · Research · published 2026-11-03T13:00:00Z

Canonical: https://digestai.news/story/8-llm-inference-frameworks-you-should-try-in-2026

## Summary

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), **Ollama** (simple CLI for local serving), and **MLC-LLM** (compiler-based for cross-platform deployments). Each framework addresses trade-offs between throughput, latency, hardware compatibility, and ease of deployment, with examples like continuous batching, paged KV cache management, and low-precision quantization. The article advises developers to select tools based on their traffic patterns, hardware, and deployment targets, emphasizing hands-on benchmarking for real-world performance.

## 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

## Why it matters

Frameworks like vLLM and SGLang optimize for high-throughput data centers, while tools like llama.cpp and Ollama simplify edge or local deployments, broadening access for smaller teams.

## Sources

1. [8 LLM Inference Frameworks You Should Try in 2026](https://kdnuggets.com/8-llm-inference-frameworks-you-should-try-in-2026) (KDnuggets, 2026-11-03)

## Cite

Digest AI, "8 LLM Inference Frameworks You Should Try in 2026", 3 November 2026, https://digestai.news/story/8-llm-inference-frameworks-you-should-try-in-2026

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