Local LLM

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. ...

Choosing a local LLM starts with the exact model identity and the resources it needs. This guide covers the families discussed in the original article and explains how to evaluate their fit. It does not present a locally measured benchmark leaderboard. Correction — October 7, 2026: Llama 3.3 was incorrectly described as an 8B model, and Mistral Small 3 as a 7B model. Their correct sizes are 70B and 24B. The previous hardware-speed table, blanket privacy claims and inconsistent cloud-cost calculations have also been removed or corrected. ...

Aider + Ollama gives you a fully local AI pair programmer that costs nothing to run, sends zero code to any cloud, and works completely offline — set it up once and you have a private coding assistant running on your own hardware. Why Local AI Coding Matters in 2026 Local AI coding matters in 2026 because the economics and privacy calculus have fundamentally shifted. Stack Overflow’s 2025 developer survey found that 84% of developers use or plan to use AI coding tools, with 51% using them daily — but cloud AI subscriptions add up fast. GitHub Copilot runs $10–19/month per seat; Claude API costs $15–75 per million tokens at the high end. For teams or solo developers processing large codebases, those costs compound quickly. Meanwhile, 91% AI adoption across 135,000+ developers in active repos (DX Q4 2025) means organizations are scrutinizing what code actually leaves their networks. Financial services, healthcare, and defense contractors operate under strict data residency rules that make cloud AI assistants a compliance liability. Local models eliminate both problems simultaneously: the API bill drops to zero, and proprietary code never touches an external server. The AI code assistant market hit $3–3.5 billion in 2025 (Gartner), which means the tooling to run serious models locally has matured — Ollama now supports 100+ models, and quantized 7B parameter models run comfortably on a 16GB RAM MacBook M-series chip. ...

The right answer depends entirely on your scale: Ollama is the fastest path from zero to running a local LLM (2 minutes, zero config), LM Studio is the best option if you’re on integrated graphics or want a GUI, and vLLM is the only serious choice once you need to serve more than one user concurrently — it delivers up to 16x higher throughput than Ollama under load. Why Developers Are Moving from Cloud APIs to Local Inference Local LLM deployment is not a niche experiment anymore. The market is projected to grow 42% in 2026 as developers calculate the real cost of API calls at scale and start weighing data privacy risks. When you’re running a coding assistant for a team of 30 engineers, sending every keystroke completion to OpenAI adds up fast — both financially and contractually. The shift is also driven by model quality: open-weight models like Llama 3.3, Mistral, and Devstral have closed most of the capability gap with commercial frontier models for code-heavy workloads. In 2025–2026, Ollama adoption alone grew 300% by developer survey data (JetBrains AI Pulse), making it the default entry point for local inference. But adoption data also shows a clear pattern: 80% of developers start with Ollama for experimentation, then hit a scaling wall when they try to share the instance with their team. That’s the moment the “which stack” question becomes urgent. ...

You do not need to pay for cloud AI APIs anymore. Ollama and LM Studio let you run powerful language models entirely on your own hardware — for free, with full privacy, and with zero per-request cost. Ollama is the developer’s tool: a CLI that deploys models in one command and serves them via an OpenAI-compatible API. LM Studio is the explorer’s tool: a polished desktop app with a built-in model browser, chat interface, and visual performance monitoring. Both use llama.cpp under the hood, so raw inference speed is nearly identical. Most power users in 2026 run both — LM Studio for experimenting with new models, Ollama for production integration. ...