{"version":1,"type":"story","url":"https://digestai.news/story/8-llm-inference-frameworks-you-should-try-in-2026","json":"https://digestai.news/story/8-llm-inference-frameworks-you-should-try-in-2026.json","markdown":"https://digestai.news/story/8-llm-inference-frameworks-you-should-try-in-2026.md","slug":"8-llm-inference-frameworks-you-should-try-in-2026","headline":"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.","keyPoints":["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"],"whyItMatters":"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.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":["Modular","NVIDIA","Apache TVM"],"models":[],"people":["Bala Priya"]},"firstPublishedAt":"2026-11-03T13:00:00Z","updatedAt":"2026-11-03T13:00:00Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"KDnuggets","title":"8 LLM Inference Frameworks You Should Try in 2026","url":"https://kdnuggets.com/8-llm-inference-frameworks-you-should-try-in-2026","publishedAt":"2026-11-03T13:00:00Z","type":"press","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"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","publisher":"Digest AI","title":"8 LLM Inference Frameworks You Should Try in 2026","datePublished":"2026-11-03T13:00:00Z","url":"https://digestai.news/story/8-llm-inference-frameworks-you-should-try-in-2026"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}