Local-Llm

The Rapid-MLX DSH Provider is a native npm plugin for the DeepSeek Harness (dsh) that lets the agent read its model configuration straight from a running Rapid-MLX server’s /v1/models endpoint instead of hand-written settings.yaml. Instead of copying context windows, reasoning efforts, and max_model_len values by hand — and re-copying them every time you switch models — the provider serves those facts directly from Rapid-MLX’s JSON schema, keeps reasoning control truthful, and compacts the context at the real memory limit of your Apple Silicon machine. ...

The best self-hosted AI agent platform for personality and long-term memory in 2026 is Open WebUI if you want an out-of-the-box conversational memory and per-chat personas with a single-binary install, Letta if you want agents that self-edit a persistent memory core, and mem0 if you want memory bolted onto an existing app. None of them alone delivers a truly zero-dependency deploy combined with a memorable, durable personality — that gap is where the real opportunity lies. ...

You can absolutely use AI to code at home without going broke. By combining free-tier cloud tools like GitHub Copilot Free with open-source local models running on Ollama and Continue.dev, indie developers can build a fully functional AI coding assistant for $0 per month. A one-time investment of $200–500 in a used GPU or an Apple Silicon Mac unlocks local models that match cloud services on most everyday coding tasks, saving you hundreds of dollars per year compared to subscriptions like Cursor Pro or Claude Team. ...

The open-source AI developer tools ecosystem in 2026 has matured to the point where every proprietary AI service — from GitHub Copilot to Pinecone to ChatGPT — has a viable, production-ready open-source alternative. This directory covers 18+ categories of open-source AI devtools, including coding agents, local inference engines, agent frameworks, vector databases, RAG pipelines, evaluation platforms, and observability tools, with maturity badges and direct comparisons to the closed-source tools they replace. ...

Ollama is an open-source local LLM runtime that exposes a REST API on http://localhost:11434, letting you run Llama 4, Qwen3, DeepSeek R1, Gemma 4, and 4,500+ other models entirely on your machine — with zero per-token cost and no data leaving your network. The OpenAI-compatible /v1/ layer means most existing SDK code works after a one-line base_url change. Why Local LLMs Went Mainstream in 2026 Local LLM adoption crossed a meaningful threshold in 2026, driven by economics, privacy regulation, and dramatically improved model quality in small footprints. Ollama surpassed 170,000 GitHub stars — the most starred local LLM runtime project on the platform — and monthly downloads grew from 100K in Q1 2023 to 52 million in Q1 2026, a 520x increase in three years. The stat that matters most for developer decision-making: 42% of developers now run at least some LLM workloads entirely on local machines, up from single digits in 2023. The economic case is straightforward — a team of five developers can spend $3,000–$30,000 in cloud LLM API costs over a three-month development cycle before shipping a single production feature. Local inference eliminates that cost entirely during the iteration phase. HuggingFace now hosts 135,000 GGUF-formatted models optimized for local inference, up from just 200 three years ago, giving developers access to a deep catalog. For regulated industries — healthcare, finance, government — local deployment isn’t just economical, it’s frequently mandatory: patient data, financial records, and classified documents cannot traverse cloud APIs. Ollama handles this by design. ...

The best self-hosted AI coding assistant in 2026 depends entirely on your team size and hardware: Tabby for compliance-constrained enterprises, Continue + Ollama for individuals and teams under ~39 people who want zero cost, and Void should be avoided until its development resumes—it’s been paused since mid-2025. Why Developers Are Going Self-Hosted in 2026 Self-hosted AI coding assistants have moved from niche curiosity to serious enterprise consideration in 2026, driven by three converging forces. First, GitHub Copilot shifted to usage-based billing starting June 1, 2026, and raised Copilot Enterprise to $39/user/month—a 2.6x increase that immediately restarted budget conversations. Second, 38% of Fortune 500 companies that deployed AI coding assistants have already experienced security incidents related to these tools, according to Digital Applied’s January 2026 report. Third, European regulations created an irreconcilable conflict: the CLOUD Act and FISA Section 702 allow US government access to data on US-controlled infrastructure, while GDPR Article 48 prohibits transferring EU data to foreign jurisdictions without legal grounds. Microsoft admitted it cannot guarantee EU data inaccessibility to US government requests—making GitHub Copilot and Claude Code an active legal risk for EU fintech and healthcare companies. Meanwhile, open-source models have caught up: Qwen2.5-Coder 32B scores 92.7% on HumanEval, exceeding GitHub Copilot’s estimated ~75%. The quality argument for cloud-only tools is gone. ...

Gemma 4 is Google’s family of open-weights models released April 2, 2026 under Apache 2.0 — four sizes from a 2B mobile-ready model to a 31B dense powerhouse, all runnable locally without sending a single byte to Google’s servers. This guide covers every deployment path: Ollama, LM Studio, Hugging Face Transformers, llama.cpp, Android, and iOS. What Is Gemma 4 and Why Run It On-Device? Gemma 4 is Google DeepMind’s fourth-generation open-weights language model family, released on April 2, 2026 under the Apache 2.0 license with no commercial restrictions. The family spans four sizes — E2B (~2.3B effective parameters), E4B (~4.5B), 26B MoE (only 3.8B active per token), and 31B Dense — each capable of running entirely on consumer hardware. At the top end, the 31B model scores 85.2% on MMLU Pro and 81.8% on HumanEval; the 26B MoE model sits at Arena AI ELO rank #3 globally at 1452 — all while being something you can run on a gaming laptop. Running Gemma 4 on-device eliminates API costs entirely, replacing per-token billing with a one-time GPU investment. More importantly, inference stays local: code, documents, customer data, and proprietary context never leave your machine. For enterprises bound by HIPAA, SOC 2, or internal data governance rules, that’s not optional — it’s the whole point. Apache 2.0 also means you can fine-tune on proprietary data and redistribute the result commercially, without any restrictions that come with Meta’s Llama license or Mistral’s community terms. ...

Qwen3 32B fits on a single 24GB GPU using Q4_K_M quantization — it takes roughly 19.8GB VRAM, leaving ~4GB free for the KV cache. Install Ollama, run ollama pull qwen3:32b, and you have a frontier-class model running entirely on your hardware in under 10 minutes. What Is Qwen3 32B and Why Run It Locally? Qwen3 32B is the largest dense (non-MoE) model in Alibaba’s Qwen3 family, released in April 2026. Unlike the 235B MoE variant that demands multiple high-end GPUs, the 32B fits comfortably on consumer hardware at the right quantization level. The model scores competitively with Claude Sonnet 4.5 on coding benchmarks when run locally on an RTX 5070 at Q4 quantization (~40 tokens/sec), making it the most capable model that a single 24GB GPU can fully accelerate. At FP16 precision the model weighs ~64GB and needs ~64GB VRAM — far beyond a single consumer card. But at Q4_K_M quantization that drops to ~19.8GB, slotting neatly into a 24GB card with headroom to spare. Running it locally eliminates per-token API costs, keeps sensitive data on your machine, and removes rate-limit friction from high-throughput workloads. For developers who send thousands of requests per day, the break-even against cloud API pricing is typically under two months of GPU electricity costs. The 131K-token context window is fully supported locally, though longer contexts reduce throughput by 10–20% per doubling. ...

Gemma 4 31B Dense runs locally on a single RTX 4090 or Mac M3 Max using Ollama — no API key, no data leaving your machine. Install Ollama, run ollama pull gemma4:31b, and you have a model that scores 87.1% on MMLU, beating GPT-4o’s 86.5%, running entirely on your hardware. What Is Gemma 4 31B Dense and Why Run It Locally? Gemma 4 31B Dense is a 31-billion-parameter language model released by Google DeepMind on April 2, 2026, under the Apache 2.0 license. Unlike mixture-of-experts architectures that distribute parameters across sparse expert layers, the 31B Dense model activates all 31 billion parameters on every token — giving it more reliable reasoning depth than larger MoE models with similar active parameter counts. In benchmark testing, Gemma 4 31B scores 87.1% on MMLU (beating GPT-4o’s 86.5%), 89.2% on AIME 2026, and 84.3% on GPQA Diamond — outperforming Llama 4 Scout’s 109B MoE model on the harder science benchmarks. Running it locally means zero API costs, complete data privacy, no rate limits, and the ability to integrate with any tool via the OpenAI-compatible REST endpoint that Ollama exposes on localhost:11434. For developers, researchers, or privacy-conscious users, this is the highest-performing open model available for on-device inference as of mid-2026. ...

The best local LLM models in 2026 are Llama 3.3 8B (best instruction following), Qwen 2.5 14B (best coding), Phi-4 (best math reasoning per GB), Mistral Small 3 7B (fastest inference), and DeepSeek R1 (best chain-of-thought reasoning). Each runs offline on consumer hardware using Ollama or LM Studio. Why Run LLMs Locally in 2026? (Privacy, Cost, and Control) Running LLMs locally in 2026 means your data never leaves your machine — no API logs, no third-party retention, no rate limits. This is the primary driver behind the shift: over 80% of enterprises are expected to have deployed generative AI models by 2026 (up from under 5% in 2023), and a significant portion are choosing on-premise or local inference to meet compliance requirements around GDPR, HIPAA, and financial data regulations. Beyond privacy, local inference eliminates per-token costs entirely — at scale (more than 50 million tokens per month), the break-even against cloud APIs is 3.5 to 69 months depending on hardware spend, with upfront costs ranging from $40,000 to $190,000. For individual developers, the math is simpler: a one-time GPU purchase runs models indefinitely for $0/token. Local inference also removes dependency on third-party uptime, rate limits, and pricing changes. In 2026, consumer hardware can run GPT-4-class models without compromise. ...