Local LLM Guides

Compare local AI video generators on quality, speed and VRAM, with RTX 4090 benchmarks, example clips and an Atomic Chat setup guide.

Compare 10 uncensored LLMs to run locally in 2026, including Ollama builds and abliterated models, with setup advice and benchmarks.

See how the best local LLMs for coding compare across benchmarks, which model we recommend for different use cases, and the key takeaways from our testing.

Compare local AI image generators, with seven models benchmarked on an RTX 5090. See image examples, generation speed, memory use, and license limits.

The 10 best local LLM apps in 2026, compared on interface, platform reach, openness, and tool support — and which one to start with.

Cloud AI leaks data and goes down. Offline AI runs local LLMs on your own machine. A practical guide to hardware, models, and setup that works.

The best local LLMs for 32GB of VRAM or RAM in 2026: which quant to pick, exact file sizes, benchmarks, and how much context each model leaves you.

Qwen 3.8 27B runs from 12 GB up. Pick the Atomic Dynamic GGUF that fits your hardware, then run it locally with Atomic Chat or llama.cpp. Benchmarks included.

Compare local LLMs for 8GB RAM or VRAM, including Qwen 3.5, GLM-4.6V-Flash, Phi-4 Mini and Gemma 3, with model sizes and quantization choices.

The best local LLMs for 16GB of VRAM or RAM in 2026: the quant to pick, file sizes, benchmarks, and how much context each model really leaves you.

Explore GLM-5.3-Flash, a 320B MoE with 18B active parameters: local hardware requirements, benchmarks, and available runtime support.

What abliterated models are, how refusal removal actually works, how they differ from uncensored and jailbroken models, and how to run one locally.

Run DeepSeek Harness on a local model, step by step: Atomic Chat as the OpenAI-compatible provider, plus permissions, plugins, and fixes for the errors we hit.

What an MCP server is, how the Model Context Protocol works, and how to set up, test, and securely use local and remote MCP servers in an AI app.

Step-by-step guide to running Claude Code with a local LLM: install the agent, connect it to Atomic Chat, Ollama, llama.cpp, or LM Studio, and work offline.

Step-by-step guide to running a self-hosted LLM with Atomic Chat: hardware requirements, the best open models of 2026, and how to expose a local API.

Learn how LLM KV cache grows with context. Estimate RAM and VRAM with our calculator and explore TurboQuant compression data.

DeepSeek V4 Flash needs 70–162 GB on disk. Pick the Atomic Dynamic GGUF that fits your memory, then run it locally with Atomic Chat or llama.cpp.

Run Ling 3.0 Flash on your own machine: hardware requirements, Atomic Dynamic GGUF builds, and setup with Atomic Chat or the TurboQuant llama.cpp build.

Run GLM locally with Atomic Chat: pick the right GLM-4.7-Flash or GLM-5.2 build for your hardware, download a GGUF, and chat entirely offline.

Learn how to run Qwen locally: pick the right model for your hardware, download the best GGUF quantization, and chat offline using Atomic Chat.

Run Kimi K3 locally: hardware requirements, Atomic Chat setup, renting 8x B300 GPUs on Vast, real costs, and the errors I hit along the way.

Run Google’s Gemma 4 models on your own machine. Pick the right size for your RAM or VRAM, choose a GGUF quantization, and start chatting locally.

Learn about GGUF, the file format for local AI models: how it works, what Q4_K_M means, and how to choose a quantization.

NVFP4 is a 4-bit NVIDIA format for smaller LLMs and faster inference on Blackwell GPUs. Learn how its block scaling and quantization work.

Is Ollama safe? Review local LLM security risks and learn how to check and harden your Ollama setup.

Compare Claude Sonnet 5.5 with Opus, Fable and GPT-6 Astra, then choose a local Qwen, Ornith or Bonsai model for your hardware.

Six local LLM builds tested on RTX 3090: speed, VRAM, context limits and coding demos. Compare exact GGUF files, including ternary Bonsai 2.

Compare eight Claude Code alternatives by local-model support, interface, code access, and approvals. Includes OpenCode, Kilo Code, Codex, and more.

Compare the 8 best local AI agents in 2026: Atomic Agent, Kilo Code, OpenClaw, Hermes, Claude Code, Cline, Goose, and pool. See setup and privacy.

Run Qwen3.8 Flash Next uncensored locally from 80 GB up. Compare the community abliterations, pick the GGUF that fits your memory, then run it in Atomic Chat.

Qwen3.8 Flash Next runs from 64 GB of RAM up with its n-gram table on SSD. Pick the Atomic Dynamic GGUF that fits, then run it with Atomic Chat or llama.cpp.

GGUF vs MLX on Mac: why tok/s is a misleading metric, how prefill determines real speed, and benchmarks across 5 runtimes on M1 Max and M5 Max.

6 local LLMs that fit a 16GB Mac in 2026, with token speeds from public benchmarks, RAM usage, and a short guide to running them.

Run MiMo-V2.6 9B locally: compare GGUF files, plan RAM and VRAM, and configure llama.cpp. Includes benchmarks and Atomic Chat compatibility.

Bonsai 2 27B is a ternary GGUF build of the Qwen 3.8 27B language model, from 5.95 GB. Hardware requirements, benchmarks and the llama.cpp fork that runs it.

We measured six local LLMs on a real RTX 5090: tokens per second, VRAM at 32k and 128k, and NVFP4 gains. No estimates from memory bandwidth.

EXL3 vs GGUF tested on an RTX 5090: compare quantization quality, prompt speed, token generation, and VRAM, with a working ExLlamaV3 setup.

Ornith 1.5 uncensored runs from 6 GB up. Compare the community abliterations of the 9B and the 35B, then run one locally with Atomic Chat or llama.cpp.

Run Qwen 3.8 27B uncensored locally from 12 GB with a low-bit GGUF. Use Q4_K_M on a 24 GB GPU or 4-bit MLX on a 32 GB Mac.

Ornith 1.5 35B runs from a 12 GB GPU up with expert offload. Pick the Atomic Dynamic GGUF that fits, then run it locally with Atomic Chat or llama.cpp.

Ornith 1.5 9B runs from 6 GB up, 4 GB text-only. Pick the Atomic Dynamic GGUF that fits your hardware, then run it locally with Atomic Chat or llama.cpp.

Learn how speculative decoding accelerates LLM generation by drafting and verifying tokens, and how Atomic Chat uses it.

Learn how to run an LLM locally on your computer or Mac — pick a model for your hardware, understand quantization, and set it up in a few clicks, for free.

We benchmarked Ollama and llama.cpp with the same model and hardware. Compare speed, setup, interfaces, and see which local LLM runner fits you.

How to run DeepSeek R1 locally and offline — which distilled sizes fit your hardware, and step-by-step setup with Atomic Chat or Ollama.

Eight local LLMs for 12GB VRAM: exact GGUF files, RTX 3080 Ti speed and memory figures, plus Snake and physics tests on an RTX 4070.

Can you run Jev 1.13 locally? Learn how it works, see our Jev vs Laya Tetris demo, and set up Laya as an independent local alternative.

Compare seven local embedding models for RAG by retrieval quality, indexing speed, query latency, memory use, and licensing.

Six local LLMs tested on an RTX 4090: generation speed, VRAM at 32k, context limits and coding demos. Compare exact GGUF files for a 24 GB GPU.

How to run AI agents locally for free: the best local models for agentic coding, hardware requirements, and a step-by-step setup guide with Atomic Chat.

SLM vs LLM compared across nine local model configurations: quality on 74 tasks, generation speed, memory use, and energy for a classification task.

What does LLM temperature do? Compare 900 model responses, see examples at 0, 0.7 and 1.5, and learn how to choose settings and adjust them in Atomic Chat.

GPT-6 Astra benchmarks and open-weight alternatives. Connect your ChatGPT subscription in Atomic Chat, or run Qwen and Laguna on your own hardware.

Run Nous Research’s Hermes Agent locally, powered by a local model served through Atomic Chat — no cloud account and no API key. A step-by-step guide.

Run the OpenClaw AI agent locally for free by powering it with a local model served from Atomic Chat — no cloud, no per-token bills, fully private.

Run gpt-oss locally on your own machine. A step-by-step guide to gpt-oss-20b and gpt-oss-120b — the hardware you need and the fastest setup, fully offline.