Tagged "self-hosted-inference"
22 articles tagged self-hosted-inference, 25 March 2026 to 10 September 2026. Newest first.
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vLLM 0.29.0 Makes Model Runner V2 the Default for All Models
vLLM 0.29.0 marks a major milestone with Model Runner V2 becoming the default inference engine across all model types, bringing CUDA graph memory profiling and batch-shard optimizations to self-hosted LLM deployments.
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vLLM v0.27.0rc2 Release Candidate Available
vLLM releases v0.27.0rc2, continuing its evolution as a high-performance inference engine for local and self-hosted LLM deployment. The release candidate stage indicates maturity and readiness for production use.
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ProofCouncil: An LLM Agent for Solving Open Mathematical Problems
A new research paper introducing ProofCouncil, an LLM agent framework capable of tackling complex mathematical problem-solving, demonstrating advanced reasoning capabilities for specialized local LLM applications.
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Nvidia Isn't the Only Choice for Local LLMs Anymore, and AMD Test Proves It
A practical benchmark demonstrates that AMD GPUs are now competitive for running local LLMs, challenging Nvidia's dominance and expanding hardware options for self-hosted inference.
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Microsoft Strikes Multibillion-Dollar Deal with French AI Firm Mistral
Microsoft has announced a major investment in Mistral, a leading open-source AI company, signaling increased focus on European alternatives and open models suitable for local deployment. This partnership could accelerate the availability of efficient, locally-deployable models optimized for edge inference.
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AI Inference Costs: Build vs. Rent
An analysis comparing the economic trade-offs between building self-hosted inference infrastructure versus renting cloud-based AI services, with implications for deployment strategy decisions.
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How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured)
A practical analysis measuring the actual operational costs of running local LLMs in euros per million tokens, providing real-world benchmarks for self-hosted inference economics.
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Rapid Rise of Open Source Models in the U.S.: Nvidia Nemotron Ultra Grows Quickly on Ollama
Nvidia's Nemotron Ultra model is experiencing rapid adoption on Ollama, signaling strong momentum for open-source LLMs optimized for local deployment. The trend reflects growing demand for locally-runnable alternatives to proprietary cloud models.
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Ollama vs LM Studio vs Jan: Free Local LLM Frameworks Compared
A comprehensive comparison of three leading open-source frameworks for running large language models locally in 2026, evaluating their features, performance, and ease of use for self-hosted inference.
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Running AI Locally, Part 2: From VMware Context to Hands-On Tools
The second installment in a series covering practical approaches to running AI workloads locally, including virtualization context and hands-on tooling recommendations for self-hosted inference.
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Helmholtz AI: Democratising AI for a Data-Driven Future
The Helmholtz AI initiative focuses on making advanced AI accessible for research and practical applications through open approaches. Their framework supports distributed and local deployment models for scientific computing.
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Show HN: LiveHere – AI Videos with Self-Hosted Nvidia Cosmos on H200 GPUs
A project demonstrates self-hosted video generation using Nvidia Cosmos running on H200 GPUs, showcasing practical infrastructure for local large-scale AI model deployment. This bridges the gap between consumer-grade local inference and enterprise-scale self-hosted systems.
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Critical Out-of-Bounds Read Vulnerability Discovered in Ollama
A significant security vulnerability (CVE-2026-7482) has been identified in Ollama, affecting local LLM deployments. Users running self-hosted Ollama instances should prioritize updating to patched versions.
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Quest to Becoming AI Independent: Local Deployment Movement
Community discussion on achieving AI independence through local model deployment, reflecting growing interest in self-hosted inference infrastructure.
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Building a Remote-Accessible Local LLM Server on Raspberry Pi
A practical guide demonstrating how to deploy and access a local LLM server running on a Raspberry Pi from anywhere, combining edge deployment with convenient remote access.
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NVIDIA Adds Day-0 DeepSeek V4 Blackwell Support
NVIDIA has announced immediate support for DeepSeek V4 on Blackwell GPUs, enabling optimized local inference for one of the latest high-performance language models on cutting-edge hardware.
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AI Quota Inflation Is No Token Effort. It's Baked In
Analysis of how API providers are inflating token quotas and pricing, highlighting the economic advantages of local LLM deployment and self-hosted inference.
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Running DeepSeek R1 Locally: Your Complete Setup Guide
SitePoint publishes a comprehensive guide for setting up and running DeepSeek R1 on local hardware, covering installation, configuration, and optimization tips for self-hosted inference.
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Show HN: I Can't Write Python. It Works Anyway – Local LLM Automation
A creative project demonstrating how LLMs can automate complex local data processing tasks, even for developers without specific language expertise. Showcases practical self-hosted inference in real-world workflows.
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MiniMax M2.7 Open-Sources Globally as Industry's First Self-Improving Model
MiniMax has open-sourced its M2.7 model globally, introducing a self-improving capability that allows the model to optimize its own performance. This release significantly expands options for local deployment of sophisticated, autonomously-improving language models.
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GPU Passthrough to LXCs in Proxmox Simplifies Local LLM Deployment
GPU passthrough to Linux containers in Proxmox offers superior performance and simplicity compared to virtual machines for running local LLMs, enabling efficient on-device inference without virtualization overhead.
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Private Brain LLM Setup on Windows PC Eliminates Need for Paid Cloud Services
A user demonstrates running a complete local LLM setup on a Windows PC, eliminating dependency on subscription services like Gemini, ChatGPT, and Claude. This practical guide showcases the viability of self-hosted inference for everyday AI tasks.