Local LLMs
Updated
Best local LLMs for May 2026 — covering the latest Ollama models (Llama 4 Scout, Qwen3, Gemma 3), LM Studio vs Jan.ai comparison, VRAM and GPU requirements for RTX 3060 12 GB and other hardware, pull commands, and beginner hardware recommendations. $0/token, full privacy, offline.
Key Takeaways
Running a local model? Your output quality depends on how you prompt it. Learn systematic techniques to get better answers from any local LLM.
PromptQuorum connects to your local LLM (Ollama, LM Studio, Jan AI) and dispatches your prompt to 25+ cloud models simultaneously — compare local vs cloud results in one view.
Try PromptQuorum free →| Model | Pull Command | VRAM | Notes |
|---|---|---|---|
| Llama 4 Scout 17B | ollama pull llama4:scout | 10 GB | Meta. Best overall quality on 12 GB VRAM |
| Qwen3 8B | ollama pull qwen3:8b | 5 GB | Alibaba. Top coding + multilingual, 8 GB GPU |
| Gemma 3 12B | ollama pull gemma3:12b | 8 GB | Google. Strong reasoning, runs on RTX 3060 |
| DeepSeek-R2 8B | ollama pull deepseek-r2:8b | 5 GB | DeepSeek. Best for math and logic, 8 GB RAM |
| Feature | Ollama | LM Studio | Jan.ai |
|---|---|---|---|
| Interface | Terminal (CLI) | Desktop GUI | Desktop GUI + chat |
| API endpoint | localhost:11434 | localhost:1234 | localhost:1337 |
| Model browser | CLI only | Built-in | Built-in |
| Best for | Developers, automation | Beginners, GUI users | Privacy-first chat |
| Setup time | 2 min | 5 min | 5 min |
Zero-to-running in under 10 minutes. OS-specific installation guides, first-model walkthroughs, and a privacy-first setup checklist for beginners. Ollama installs with a single command on macOS, Windows, and Linux. For 8 GB RAM, start with Llama 3.2 3B (Q4, ~2 GB) using `ollama pull llama3.2:3b`.
Model rankings, benchmark comparisons, and use-case winners. As of May 2026, the top locally-runnable models are Llama 4 Scout 17B (best overall, MoE architecture), Qwen3 (best coding), and Gemma 3 12B (best at 16 GB RAM). All ranked by MMLU, HumanEval, and real hardware tests.
Ollama and LM Studio each run 200+ models on macOS, Windows, and Linux. Ollama is CLI-first with a production REST API; LM Studio provides a graphical interface with a built-in model browser. Guides cover both tools plus vLLM, llama.cpp, Open WebUI, and IDE integrations.
VRAM is the primary constraint for local LLMs. A 7B model at Q4_K_M needs 4.7 GB; a 70B model needs 40 GB. Guides cover GPU selection (RTX 4070 Ti to RTX 5090), Apple Silicon, budget builds, and VRAM calculation for any model. See also: [Fastest Local LLMs for Low-End PCs](/local-llms/fastest-local-llms-low-end-pcs) for CPU-only, 4 GB, and 8 GB VRAM speed benchmarks.
Fine-tuning, RAG pipelines, quantization deep-dives, distillation, model merging, and prompt optimization for production use. LoRA reduces fine-tuning VRAM requirements from 24 GB to 8 GB. QLoRA cuts it further to 4 GB. Local RAG workflows keep sensitive data on-premises while maintaining search quality.
Multi-GPU setups, inference optimization, model serving frameworks (vLLM, TensorRT-LLM), monitoring and observability, cost audits, and regulatory compliance. Local LLMs eliminate cross-border data transfer, satisfy GDPR Article 28, and reduce licensing costs 40–80% versus SaaS.
GPU selection by budget and use case, cost per token, power efficiency, thermal design, second-hand marketplace comparisons, and warranty trade-offs. RTX 4090 (~$1600) handles 70B models; RTX 4080 (~$800) runs 13B–20B; RTX 4060 (~$300) is best value for 7B models.
Complete build guides for laptop, desktop, workstation, and server deployments. From single-GPU setups to multi-node clusters. Budget builds ($500–$1500), mid-range ($1500–$5000), and enterprise ($5000+) configurations with exact part lists and estimated throughput.
On-premises deployment for compliance (GDPR, HIPAA, APPI, CAC). Zero-knowledge architecture, air-gapped setups, and access logging. Local LLMs eliminate API vendor lock-in, reduce compliance audit burden, and protect proprietary data from SaaS providers.
Break-even analysis: local vs cloud vs subscription models. Hidden SaaS costs: overage fees, enterprise seats, audit logs. Local hardware pays for itself in 6–18 months for heavy users. ROI calculators for different workload types.
A large language model (e.g., Llama 4, Qwen3.5, DeepSeek) that runs on your own hardware instead of a cloud API. You get full privacy, offline capability, no usage limits, and zero API costs after hardware purchase.
8 GB VRAM runs 7B models at Q4 quantization. 16 GB handles 13B models comfortably. 40 GB+ (e.g., dual RTX 4090s or A100) is required for 70B models. Apple Silicon unified memory counts as VRAM.
Ollama is a CLI tool that runs models via simple terminal commands and exposes an OpenAI-compatible API at `localhost:11434`. LM Studio provides a desktop GUI, model browser, and built-in chat interface. Both support the same models.
On coding and reasoning tasks, Llama 4 Scout, DeepSeek V3, and Qwen3 score within 5–10% of GPT-4o mini on standard benchmarks (MMLU, HumanEval). Claude Opus 4.7 and GPT-4o maintain an edge on complex multi-step tasks.
Fine-tuning requires 500+ labeled training examples, the QLoRA framework (reduces VRAM requirement via 4-bit quantization), 24 GB+ VRAM (or a cloud GPU rental), and 1–4 hours of training time for a 7B model.
Minimum: 8 GB RAM and any modern CPU (runs 3B–7B models at 2–5 tokens/sec). Recommended: a GPU with 8 GB+ VRAM (RTX 3060 or newer) for 20–40 tokens/sec on 7B models.
Yes. Ollama and LM Studio are free and open-source. The models themselves (Llama, Mistral, Qwen, DeepSeek) are available under open-source licenses at no cost. The only cost is your hardware.
Qwen3-Coder 7B is the top performer for code completion and review on consumer hardware (8 GB VRAM). DeepSeek-Coder V2 Lite is the strongest alternative. For CPU-only setups, Phi-3.5 Mini offers the best coding quality under 4 GB RAM.
Yes. Any modern CPU can run 3B–7B models at Q4 quantization using Ollama (CPU mode) or LM Studio. Typical CPU inference speed: 2–8 tokens/sec on a modern laptop CPU, compared to 20–50 tokens/sec on an RTX 4060. 7B Q4 requires ~5 GB RAM (not VRAM). For CPU-only setups, Phi-3.5 Mini (3.8B) and Llama 3.2 3B offer the best quality-to-speed ratio.
Ollama: run `ollama pull <model-name>` again — it downloads only changed layers. LM Studio: open the model browser, find the updated version, and download it. Old GGUF files are not automatically removed — delete them manually from ~/.ollama/models (Ollama) or ~/Library/Application Support/LM Studio/models (macOS) to free disk space. Model updates from Meta, Alibaba, and Mistral typically arrive within 24–48 hours of official release.
Top Ollama models for May 2026: Llama 4 Scout 17B (best overall on 12 GB VRAM, `ollama pull llama4:scout`), Qwen3 8B (best coding, `ollama pull qwen3:8b`, 5 GB VRAM), Gemma 3 12B (strong reasoning on RTX 3060, 8 GB VRAM), and DeepSeek-R2 8B (best math/logic, 5 GB VRAM). Run any model with `ollama run <name>` after pulling.
The RTX 3060 12 GB VRAM is an excellent local LLM GPU. Best choices: Llama 4 Scout 17B at Q4 (~10 GB VRAM, `ollama pull llama4:scout`), Gemma 3 12B (~8 GB VRAM), or Qwen3 14B (~9 GB VRAM). All run at 20–40 tokens/sec. The 12 GB VRAM puts you above the RTX 3060 Ti (8 GB) and opens up 13B-class and 17B MoE models at full quality.
Use Ollama if you want a CLI tool with an OpenAI-compatible API at localhost:11434 — best for developers and automation. Use LM Studio if you want a desktop GUI, built-in model browser, and chat interface — best for beginners. Use Jan.ai if you want a privacy-focused chat app with a built-in model store. All three support the same GGUF models. Setup time: Ollama 2 min, LM Studio 5 min, Jan.ai 5 min.
Best budget GPUs for local LLMs: RTX 3060 12 GB (~$250 used) runs 13B models at 20–30 tok/s. RTX 4060 8 GB (~$300 new) runs 7B at 35–45 tok/s. RTX 3080 10 GB (~$350 used) handles 13B comfortably. For sub-$200: RTX 2070 8 GB runs 7B models at 15–20 tok/s. AMD RX 6700 XT 12 GB (~$200 used) is comparable to RTX 3060 with ROCm on Linux. Minimum recommended: 8 GB VRAM for useful 7B inference.
Local LLMs process all data on-premises. When combined with full-disk encryption and access logging, on-premises inference satisfies GDPR Article 28 (no data processor agreement needed if data never leaves the machine). Ollama binds to `localhost` by default — no external exposure.
Japan's Act on the Protection of Personal Information (APPI) restricts cross-border data transfer for personal data. Local LLMs eliminate cross-border transfer entirely. METI's 2024 AI governance guidelines encourage privacy-preserving AI — local deployment is aligned with these recommendations.
The Cyberspace Administration of China's Interim Measures for Generative AI Services (2023) require AI providers offering services to Chinese users to register. Local LLMs running entirely on-premises are outside the CAC's public-facing provider definition, significantly reducing compliance burden for enterprise deployments.
The slide deck below covers hardware requirements (8 GB VRAM for 7B models, 40 GB+ for 70B), top open-source models 2026, Ollama setup in 5 minutes, Q4_K_M quantization, regional compliance (GDPR, APPI), and key takeaways. Download the PDF as a Local LLMs quick-reference card.
Download Local LLMs Reference Card (PDF)A local LLM is a large language model that runs entirely on your own hardware — CPU, GPU, or Apple Silicon — without sending data to external servers. You download the model file (typically 2–40 GB) and run it using a tool like Ollama or LM Studio. As of May 2026, the most popular local LLM is Meta Llama 4 Scout 17B, which runs on machines with 10 GB VRAM at 10–80 tokens/sec.
For privacy and cost, yes. For raw output quality, no. As of 2026, frontier cloud models (GPT-4o, Claude Opus 4.7) outperform all locally-runnable models on complex reasoning. However, local 70B models (Llama 4 Scout, Qwen3 72B) match or exceed GPT-4o mini on most everyday tasks — at zero per-query cost.
Minimum: 8 GB RAM to run a 7B model at Q4 quantization. Recommended: 16 GB for 13B models, 40+ GB for 70B models. Apple Silicon unified memory counts fully toward this — an M3 Mac with 18 GB can run a 13B model well. GPU VRAM is equivalent to RAM for GPU inference.
Install Ollama (ollama.com), then run one command: `ollama run llama3.1:8b`. The model downloads automatically and you can start chatting in under 5 minutes. No API key, no account, no internet connection after the initial download.
Meta Llama 4 Scout 17B for general use (Llama Community License, 10 GB VRAM). Qwen3-Coder 32B for coding (92.7% HumanEval, 20 GB VRAM). DeepSeek-R2 8B for reasoning (MIT licence, 5 GB VRAM). All are free, open-weight, and available via `ollama pull`.
Yes. When running with Ollama or LM Studio, your prompts, documents, and responses never leave your machine. No data is transmitted to any server. This makes local LLMs the recommended choice for GDPR-regulated workflows, legal and medical document processing, and any task involving confidential or personal information.
Related: Prompt Engineering Guide
Running a local model is step one. Getting great output from it is step two. The Prompt Engineering guide covers 80 techniques across 9 topics — from fundamentals like temperature and context windows to advanced methods like chain-of-thought, RAG, and team governance. Every technique works with local models.
