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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - AronDaron/dataset-generator: No-code desktop app for generating high-quality synthetic datasets to fine-tune LLMs — plan-then-execute pipeline, LLM-as-judge, HuggingFace upload. I thought I had a bug GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace GitHub - Nyquest-ai/nyquest-rust-fullstack-pub: Nyquest — Semantic Compression Proxy for LLMs. 350+ rules, local LLM stage, 15-75% token savings. Full Rust stack. GitHub - TheoV823/mneme: Enforce architectural decisions in AI-assisted development. GitHub - klemenvod/TokenBrawl: A 1v1 Bomberman-style game where two LLM agents play autonomously against each other. No human plays — you watch the AIs fight. Each agent receives a text description of the board state, reasons about it, and outputs a move as JSON. The game engine executes it. Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow Power Circuit AI: Designing Power Electronic Circuits for Motor Drives with Generative Artificial Intelligence Ask HN: How to program with IDE and LLM on CPU locally? Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis Bonsai 1-bit WebGPU - a Hugging Face Space by webml-community The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows Ask HN: Simple tooling for local LLM code critique without IDE integration? Can a General LLM Diagnose a DICOM Slice? A 10-Case Public Benchmark Charts-of-Thought: Enhancing LLM Visualization Literacy (PDF, 2026) GitHub - Mesh-LLM/mesh-llm: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. GitHub - seamus-brady/springdrift: A persistent runtime for long-lived LLM agents Writing an LLM from scratch, part 32k -- Interventions: training a better model locally with gradient accumulation Ask HN: Which LLM model and agentic CLI are you using for local development? GitHub - wayneColt/modelcascade: Route local. Escalate smart. Never overspend. Open-source multi-model cascade routing for autonomous agents. LLM pricing is 100x harder than you think GitHub - asakin/llm-primer: Pre-warmed Claude Code sessions in tmux. No startup wait. GitHub - EggerMarc/chat-rs: A multi-provider LLM framework for Rust. GitHub - SynapseKit/SynapseKit: Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS. A Claude Skill that Makes LLM Paragraphs More Bearable Does Gas Town 'steal' usage from users' LLM credits & paid services to improve itself? What's Claude Code Actually Doing? Open the Black Box with the Arthur Engine Milla Jovovich's New Open Source LLM Memory App and the Dark Code Problem Your intuition of LLM token usage might be wrong Show HN: Bloomberg Terminal for LLM ops – free and open source GitHub - 0xchamin/mcptube: Transform YouTube videos into a compounding knowledge base with transcripts, vision analysis, and agentic search. Works as an MCP server for Claude, Copilot & more. Show HN: Open KB: Open LLM Knowledge Base Your LLM is a compiler, not a runtime GitHub - sapountzis/Unslop: A Web Feed That Deserves You crates.io: Rust Package Registry Beyond Karpathy's LLM-Wiki: The Necessity of Cognitive Governance GitHub - amitshekhariitbhu/llm-internals: Learn LLM internals step by step - from tokenization to attention to inference optimization. GitHub - parallem-ai/parallem: An expressive library for running agents with the Batch API. GitHub - stfurkan/pi-llm LLM-Wiki Show HN: Formal – Formal verification for AI-generated code using Lean 4 LRTS – Regression testing for LLM prompts (open source, local-first) LLM Wiki Skill: Build a Second Brain with Claude Code and Obsidian I built an LLM Wiki and RAG solution: here's a demo for a security KB The biggest advance in AI since the LLM Predict-Rlm: The LLM Runtime That Lets Models Write Their Own Control Flow the-synthetic-library/the-synthetic-mind at main · joshferrer1/the-synthetic-library GitHub - yisding/reviewwiggum GitHub - Donnyb369/mcp-spine: Context Minifier & State Guard — Local-first MCP middleware proxy GitHub - Beledarian/wgpu-llm: A from-scratch LLM inference engine that uses wgpu (the cross-platform WebGPU implementation) to dispatch WGSL compute shaders for every math operation a Transformer needs. No CUDA. No Python. No massive framework dependencies. Just Rust, raw shaders, and your GPU. GitHub - anitiue/Hindsight: An experience-driven self-improvement framework for LLM agents — 基于经验的 LLM Agent 自我改进框架 GitHub - stef41/lmscan: 🔍 Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline. GitHub - alainnothere/AmdPerformanceTesting: Amd Performance Testing Ask HN: Is a purely Markdown-based CRM a terrible idea? Optimized for LLM agents Context Engineering - LLM Memory and Retrieval for AI Agents | Weaviate little_helper_tui/letter.md at main · sleepyeldrazi/little_helper_tui GitHub - EvanZhouDev/umr: The Unified Model Registry for all your local AI apps. GitHub - JordanCT/VigIA-Orchestrator Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain A Taxonomy of RL Environments for LLM Agents Llama LLM Network Feture GitHub - genedeng-ca/ai-mac-migration: AI-powered Mac-to-Mac migration tool - replace Apple Migration Assistant with intelligent, selective transfer using local LLMs GitHub - lunargate-ai/gateway: High-performance self-hosted AI gateway (OpenAI-compatible) with routing, retries, and streaming GitHub - AuthBits/webmcp: A lightweight, prompt-driven MCP web research server for high-quality LLM powered information extraction. Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents HUOZIIME: An On-Device LLM-enhanced Input Method for Deep Personalization TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.
kristianpaul · 2026-04-19 · via Hacker News - Newest: "LLM"

TensorRT LLM optimizes inference for LLMs and Visual Gen models with specialized kernels for common operations, an efficient runtime, and a pythonic framework that enables you to customize and extend the system.

Tech Blogs

  • [04/03] DWDP: Distributed Weight Data Parallelism for High-Performance LLM Inference on NVL72 ✨ ➡️ link

  • [03/16] Optimizing MoE Communication with One-Sided AlltoAll Over NVLink ✨ ➡️ link

  • [03/04] Sparse Attention in TensorRT LLM ✨ ➡️ link

  • [02/06] Accelerating Long-Context Inference with Skip Softmax Attention ✨ ➡️ link

  • [01/09] Optimizing DeepSeek-V3.2 on NVIDIA Blackwell GPUs ✨ ➡️ link

Previous Blogs * [10/13] Scaling Expert Parallelism in TensorRT LLM (Part 3: Pushing the Performance Boundary) ✨ [➡️ link](https://nvidia.github.io/TensorRT-LLM/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.html)
  • [09/26] Inference Time Compute Implementation in TensorRT LLM ✨ ➡️ link

  • [09/19] Combining Guided Decoding and Speculative Decoding: Making CPU and GPU Cooperate Seamlessly ✨ ➡️ link

  • [08/29] ADP Balance Strategy ✨ ➡️ link

  • [08/05] Running a High-Performance GPT-OSS-120B Inference Server with TensorRT LLM ✨ ➡️ link

  • [08/01] Scaling Expert Parallelism in TensorRT LLM (Part 2: Performance Status and Optimization) ✨ ➡️ link

  • [07/26] N-Gram Speculative Decoding in TensorRT LLM ✨ ➡️ link

  • [06/19] Disaggregated Serving in TensorRT LLM ✨ ➡️ link

  • [06/05] Scaling Expert Parallelism in TensorRT LLM (Part 1: Design and Implementation of Large-scale EP) ✨ ➡️ link

  • [05/30] Optimizing DeepSeek R1 Throughput on NVIDIA Blackwell GPUs: A Deep Dive for Developers ✨ ➡️ link

  • [05/23] DeepSeek R1 MTP Implementation and Optimization ✨ ➡️ link

  • [05/16] Pushing Latency Boundaries: Optimizing DeepSeek-R1 Performance on NVIDIA B200 GPUs ✨ ➡️ link

Latest News

  • [04/03] 🎨 TensorRT LLM now supports diffusion models for visual generation ➡️ link
Previous News * [08/05] 🌟 TensorRT LLM delivers Day-0 support for OpenAI's latest open-weights models: GPT-OSS-120B [➡️ link](https://huggingface.co/openai/gpt-oss-120b) and GPT-OSS-20B [➡️ link](https://huggingface.co/openai/gpt-oss-20b) * [07/15] 🌟 TensorRT LLM delivers Day-0 support for LG AI Research's latest model, EXAONE 4.0 [➡️ link](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B) * [06/17] Join NVIDIA and DeepInfra for a developer meetup on June 26 ✨ [➡️ link](https://events.nvidia.com/scaletheunscalablenextgenai) * [05/22] Blackwell Breaks the 1,000 TPS/User Barrier With Meta’s Llama 4 Maverick ✨ [➡️ link](https://developer.nvidia.com/blog/blackwell-breaks-the-1000-tps-user-barrier-with-metas-llama-4-maverick/) * [04/10] TensorRT LLM DeepSeek R1 performance benchmarking best practices now published. ✨ [➡️ link](https://nvidia.github.io/TensorRT-LLM/blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.html)
  • [04/05] TensorRT LLM can run Llama 4 at over 40,000 tokens per second on B200 GPUs!

L4_perf

  • [03/22] TensorRT LLM is now fully open-source, with developments moved to GitHub!

  • [03/18] 🚀🚀 NVIDIA Blackwell Delivers World-Record DeepSeek-R1 Inference Performance with TensorRT LLM ➡️ Link

  • [02/28] 🌟 NAVER Place Optimizes SLM-Based Vertical Services with TensorRT LLM ➡️ Link

  • [02/25] 🌟 DeepSeek-R1 performance now optimized for Blackwell ➡️ Link

  • [02/20] Explore the complete guide to achieve great accuracy, high throughput, and low latency at the lowest cost for your business here.

  • [02/18] Unlock #LLM inference with auto-scaling on @AWS EKS ✨ ➡️ link

  • [02/12] 🦸⚡ Automating GPU Kernel Generation with DeepSeek-R1 and Inference Time Scaling ➡️ link

  • [02/12] 🌟 How Scaling Laws Drive Smarter, More Powerful AI ➡️ link

  • [2025/01/25] Nvidia moves AI focus to inference cost, efficiency ➡️ link

  • [2025/01/24] 🏎️ Optimize AI Inference Performance with NVIDIA Full-Stack Solutions ➡️ link

  • [2025/01/23] 🚀 Fast, Low-Cost Inference Offers Key to Profitable AI ➡️ link

  • [2025/01/16] Introducing New KV Cache Reuse Optimizations in TensorRT LLM ➡️ link

  • [2025/01/14] 📣 Bing's Transition to LLM/SLM Models: Optimizing Search with TensorRT LLM ➡️ link

  • [2025/01/04] ⚡Boost Llama 3.3 70B Inference Throughput 3x with TensorRT LLM Speculative Decoding ➡️ link

  • [2024/12/10] ⚡ Llama 3.3 70B from AI at Meta is accelerated by TensorRT-LLM. 🌟 State-of-the-art model on par with Llama 3.1 405B for reasoning, math, instruction following and tool use. Explore the preview ➡️ link

  • [2024/12/03] 🌟 Boost your AI inference throughput by up to 3.6x. We now support speculative decoding and tripling token throughput with our NVIDIA TensorRT-LLM. Perfect for your generative AI apps. ⚡Learn how in this technical deep dive ➡️ link

  • [2024/12/02] Working on deploying ONNX models for performance-critical applications? Try our NVIDIA Nsight Deep Learning Designer ⚡ A user-friendly GUI and tight integration with NVIDIA TensorRT that offers: ✅ Intuitive visualization of ONNX model graphs ✅ Quick tweaking of model architecture and parameters ✅ Detailed performance profiling with either ORT or TensorRT ✅ Easy building of TensorRT engines ➡️ link

  • [2024/11/26] 📣 Introducing TensorRT LLM for Jetson AGX Orin, making it even easier to deploy on Jetson AGX Orin with initial support in JetPack 6.1 via the v0.12.0-jetson branch of the TensorRT LLM repo. ✅ Pre-compiled TensorRT LLM wheels & containers for easy integration ✅ Comprehensive guides & docs to get you started ➡️ link

  • [2024/11/21] NVIDIA TensorRT LLM Multiblock Attention Boosts Throughput by More Than 3x for Long Sequence Lengths on NVIDIA HGX H200 ➡️ link

  • [2024/11/19] Llama 3.2 Full-Stack Optimizations Unlock High Performance on NVIDIA GPUs ➡️ link

  • [2024/11/09] 🚀🚀🚀 3x Faster AllReduce with NVSwitch and TensorRT LLM MultiShot ➡️ link

  • [2024/11/09] ✨ NVIDIA advances the AI ecosystem with the AI model of LG AI Research 🙌 ➡️ link

  • [2024/11/02] 🌟🌟🌟 NVIDIA and LlamaIndex Developer Contest 🙌 Enter for a chance to win prizes including an NVIDIA® GeForce RTX™ 4080 SUPER GPU, DLI credits, and more🙌 ➡️ link

  • [2024/10/28] 🏎️🏎️🏎️ NVIDIA GH200 Superchip Accelerates Inference by 2x in Multiturn Interactions with Llama Models ➡️ link

  • [2024/10/22] New 📝 Step-by-step instructions on how to ✅ Optimize LLMs with NVIDIA TensorRT-LLM, ✅ Deploy the optimized models with Triton Inference Server, ✅ Autoscale LLMs deployment in a Kubernetes environment. 🙌 Technical Deep Dive: ➡️ link

  • [2024/10/07] 🚀🚀🚀Optimizing Microsoft Bing Visual Search with NVIDIA Accelerated Libraries ➡️ link

  • [2024/09/29] 🌟 AI at Meta PyTorch + TensorRT v2.4 🌟 ⚡TensorRT 10.1 ⚡PyTorch 2.4 ⚡CUDA 12.4 ⚡Python 3.12 ➡️ link

  • [2024/09/17] ✨ NVIDIA TensorRT LLM Meetup ➡️ link

  • [2024/09/17] ✨ Accelerating LLM Inference at Databricks with TensorRT-LLM ➡️ link

  • [2024/09/17] ✨ TensorRT LLM @ Baseten ➡️ link

  • [2024/09/04] 🏎️🏎️🏎️ Best Practices for Tuning TensorRT LLM for Optimal Serving with BentoML ➡️ link

  • [2024/08/20] 🏎️SDXL with #Model Optimizer ⏱️⚡ 🏁 cache diffusion 🏁 quantization aware training 🏁 QLoRA 🏁 #Python 3.12 ➡️ link

  • [2024/08/13] 🐍 DIY Code Completion with #Mamba ⚡ #TensorRT #LLM for speed 🤖 NIM for ease ☁️ deploy anywhere ➡️ link

  • [2024/08/06] 🗫 Multilingual Challenge Accepted 🗫 🤖 #TensorRT #LLM boosts low-resource languages like Hebrew, Indonesian and Vietnamese ⚡➡️ link

  • [2024/07/30] Introducing🍊 @SliceXAI ELM Turbo 🤖 train ELM once ⚡ #TensorRT #LLM optimize ☁️ deploy anywhere ➡️ link

  • [2024/07/23] 👀 @AIatMeta Llama 3.1 405B trained on 16K NVIDIA H100s - inference is #TensorRT #LLM optimized ⚡ 🦙 400 tok/s - per node 🦙 37 tok/s - per user 🦙 1 node inference ➡️ link

  • [2024/07/09] Checklist to maximize multi-language performance of @meta #Llama3 with #TensorRT #LLM inference: ✅ MultiLingual ✅ NIM ✅ LoRA tuned adaptors➡️ Tech blog

  • [2024/07/02] Let the @MistralAI MoE tokens fly 📈 🚀 #Mixtral 8x7B with NVIDIA #TensorRT #LLM on #H100. ➡️ Tech blog

  • [2024/06/24] Enhanced with NVIDIA #TensorRT #LLM, @upstage.ai’s solar-10.7B-instruct is ready to power your developer projects through our API catalog 🏎️. ✨➡️ link

  • [2024/06/18] CYMI: 🤩 Stable Diffusion 3 dropped last week 🎊 🏎️ Speed up your SD3 with #TensorRT INT8 Quantization➡️ link

  • [2024/06/18] 🧰Deploying ComfyUI with TensorRT? Here’s your setup guide ➡️ link

  • [2024/06/11] ✨#TensorRT Weight-Stripped Engines ✨ Technical Deep Dive for serious coders ✅+99% compression ✅1 set of weights → ** GPUs ✅0 performance loss ✅** models…LLM, CNN, etc.➡️ link

  • [2024/06/04] ✨ #TensorRT and GeForce #RTX unlock ComfyUI SD superhero powers 🦸⚡ 🎥 Demo: ➡️ link 📗 DIY notebook: ➡️ link

  • [2024/05/28] ✨#TensorRT weight stripping for ResNet-50 ✨ ✅+99% compression ✅1 set of weights → ** GPUs\ ✅0 performance loss ✅** models…LLM, CNN, etc 👀 📚 DIY ➡️ link

  • [2024/05/21] ✨@modal_labs has the codes for serverless @AIatMeta Llama 3 on #TensorRT #LLM ✨👀 📚 Marvelous Modal Manual: Serverless TensorRT LLM (LLaMA 3 8B) | Modal Docs ➡️ link

  • [2024/05/08] NVIDIA Model Optimizer -- the newest member of the #TensorRT ecosystem is a library of post-training and training-in-the-loop model optimization techniques ✅quantization ✅sparsity ✅QAT ➡️ blog

  • [2024/05/07] 🦙🦙🦙 24,000 tokens per second 🛫Meta Llama 3 takes off with #TensorRT #LLM 📚➡️ link

  • [2024/02/06] 🚀 Speed up inference with SOTA quantization techniques in TRT-LLM

  • [2024/01/30] New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget

  • [2023/12/04] Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100

  • [2023/11/27] SageMaker LMI now supports TensorRT LLM - improves throughput by 60%, compared to previous version

  • [2023/11/13] H200 achieves nearly 12,000 tok/sec on Llama2-13B

  • [2023/10/22] 🚀 RAG on Windows using TensorRT LLM and LlamaIndex 🦙

  • [2023/10/19] Getting Started Guide - Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM, Now Publicly Available

  • [2023/10/17] Large Language Models up to 4x Faster on RTX With TensorRT LLM for Windows

TensorRT LLM Overview

TensorRT LLM is an open-sourced library for optimizing LLM and Visual Gen inference. It provides state-of-the-art optimizations, including custom kernels for common inference operations (attention, GEMMs, MoE, ...), algorithmic runtime optimizations (Prefill-Decode disaggregation, Wide Expert Parallelism, Speculative Decoding, etc), and much more, to perform inference efficiently on NVIDIA GPUs.

Architected on PyTorch, TensorRT LLM provides a high-level Python LLM API that supports a wide range of inference setups - from single-GPU to multi-GPU or multi-node deployments. It includes built-in support for various parallelism strategies and advanced features. The LLM API integrates seamlessly with the broader inference ecosystem, including NVIDIA Dynamo and the Triton Inference Server.

TensorRT LLM is designed to be modular and easy to modify. Its PyTorch-native architecture allows developers to experiment with the runtime or extend functionality. Several popular models are also pre-defined and can be customized using native PyTorch code, making it easy to adapt the system to specific needs.

Getting Started

To get started with TensorRT-LLM, visit our documentation:

Deprecation Policy

Deprecation is used to inform developers that some APIs and tools are no longer recommended for use. Beginning with version 1.0, TensorRT LLM has the following deprecation policy:

  1. Communication of Deprecation
  • Deprecation notices are documented in the Release Notes.
  • Deprecated APIs, methods, classes, or parameters include a statement in the source code indicating when they were deprecated.
  • If used, deprecated methods, classes, or parameters issue runtime deprecation warnings.
  1. Migration Period
  • TensorRT LLM provides a 3-month migration period after deprecation.
  • During this period, deprecated APIs, tools, or parameters continue to work but trigger warnings.
  1. Scope of Deprecation
  • Full API/Method/Class Deprecation: The entire API/method/class is marked for removal.
  • Partial Deprecation: If only specific parameters of an API/method are deprecated (e.g., param1 in LLM.generate(param1, param2)), the method itself remains functional, but the deprecated parameters will be removed in a future release.
  1. Removal After Migration Period
  • After the 3-month migration period ends, deprecated APIs, tools, or parameters are removed in a manner consistent with semantic versioning (major version changes may include breaking removals).

Telemetry Data Collection

TensorRT-LLM collects anonymous telemetry data by default. This data is used in aggregate to understand usage patterns and prioritize engineering efforts. This data cannot be traced back to any individual user. No prompts, user-identifying information, or persistent identifiers are collected. Any deployment identifiers are ephemeral, randomly generated per deployment, and not linked to users. The data we collect includes:

  • Ingress point (e.g., LLM API, CLI, serve command)
  • Deployment duration (via periodic heartbeats)
  • GPU SKUs, count, memory, and CUDA version
  • Model architecture class name (e.g., LlamaForCausalLM)
  • Parallelism configuration (TP/PP/CP/MoE-EP/MoE-TP sizes), quantization algorithm, dtype, KV cache dtype
  • System information (OS platform, Python version, CPU architecture, CPU count)
  • TRT-LLM version and backend
  • Feature flags (LoRA, speculative decoding, prefix caching, CUDA graphs, chunked context, data parallelism)
  • Disaggregated serving metadata (role and deployment ID)

Telemetry is automatically disabled in CI and test environments.

Opting Out of Telemetry Data Collection

To disable telemetry data collection, use any of the following methods:

  • Environment variable: Set TRTLLM_NO_USAGE_STATS=1, DO_NOT_TRACK=1, or TELEMETRY_DISABLED=true
  • File-based: Create the file ~/.config/trtllm/do_not_track
  • Python API: Pass TelemetryConfig(disabled=True) to LLM()
  • CLI flag: Use --no-telemetry on trtllm-serve, trtllm-bench, or trtllm-eval

The telemetry collection code is fully open source and auditable at tensorrt_llm/usage/. For a detailed field-by-field reference of exactly what is collected, see the schema documentation.

Useful Links

  • Quantized models on Hugging Face: A growing collection of quantized (e.g., FP8, FP4) and optimized LLMs, including DeepSeek FP4, ready for fast inference with TensorRT LLM.
  • NVIDIA Dynamo: A datacenter scale distributed inference serving framework that works seamlessly with TensorRT LLM.
  • AutoDeploy: A beta backend for TensorRT LLM to simplify and accelerate the deployment of PyTorch models.
  • WeChat Discussion Group: A real-time channel for TensorRT LLM Q&A and news.