Alex Shapiro

GPT-6.1 Terra specs and benchmarks, plus six open-weight alternatives you can run locally, with GGUF picks for 8 GB to 96 GB of memory.

Run Nemotron 3.5 Bolt E4B on Linux with an NVIDIA GPU. Check official GGUF benchmarks and memory figures, then use the pinned llama.cpp CUDA setup.

Run local AI on NVIDIA RTX Spark with Atomic Chat. Set up local chat, image generation and an OpenAI-compatible API.

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

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.

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

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

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

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

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

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.

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.

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.

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

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

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

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.

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.

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.

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.

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.

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.