Find Which Local LLMs
Your Hardware Can Run
Check which local LLMs fit your GPU, Mac, or PC. Compare VRAM and RAM requirements, Ollama and llama.cpp support, quantization, context length, and multi-GPU options before you download or buy.
Estimates are based on model specs, hardware memory, quantization overhead, and runtime assumptions. LocalAIRun is a planning tool, not a fake benchmark lab.
From estimate to execution
Follow a documented local AI workflow
Recipes preserve the exact hardware, model artifact, tool stack, settings, verification, and report status.
Generate an SD 1.5 image on a 16GB M4 MacBook Pro
MacBook Pro M4 16GB · Stable Diffusion 1.5 FP16 · Comfy Desktop · MPS
Run Qwen3 8B with Ollama on a 16GB Apple Silicon Mac
Apple M-series 16GB+ · Qwen3 8B Q4 · Ollama · Metal
Run Qwen3 8B in LM Studio on a 16GB Apple Silicon Mac
Apple M-series 16GB+ · Qwen3 8B Q4 · LM Studio · Metal
Check a common local AI setup
Start from a popular GPU, Mac, or model requirement instead of configuring everything from scratch.
What can an RX 7900 XTX run?
Check runnable chat and coding models with 24GB VRAM plus system RAM offload.
What can an RTX 4090 run?
Compare model quality, quantization, context headroom, and CPU offload options.
What can an RTX 5090 run?
See models that fit fully in VRAM and larger options that can use system RAM.
Models for a 192GB M3 Ultra Mac
Explore large dense and MoE models suited to MLX and llama.cpp on Apple Silicon.
Qwen3.8 27B hardware requirements
Compare its Q4 memory estimate, task fit, variants, and recommended hardware.
Gemma 4 31B hardware requirements
Review quantization, RAM and VRAM estimates, capabilities, and compatible hardware.
Pick a task, get model and hardware options
Use the planner when you know what you want to do: chat, coding, image generation, video, voice, RAG, or local agents.
Pick your hardware, see runnable models
Start with a Mac, NVIDIA GPU, AMD GPU, AI PC, or multi-GPU setup and compare what can realistically fit.
Browse open models and variants
Compare model families, parameters, active parameters, quantized artifacts, runtimes, licenses, and task capabilities.
Compare local AI hardware
Explore Apple Silicon, NVIDIA GPUs, AMD GPUs, AI PCs, and edge devices with memory, price, and fit assumptions.
Fit scores use transparent memory and runtime assumptions unless evidence says otherwise.
A Q4 GGUF artifact, FP16 checkpoint, and MLX build can fit very differently.
Use recommendations as a starting point, then manually pick model, quant, hardware, RAM, and GPU count.
Focused on Ollama, llama.cpp, MLX, Apple Silicon, consumer GPUs, and workstation hardware.
What Is a Local LLM?
A local LLM (large language model) is an AI model that runs entirely on your own computer — your CPU, GPU, or RAM — without sending any data to external servers. Unlike cloud-based AI services such as ChatGPT or Claude, a local LLM processes every prompt on your hardware, keeping your data completely private.
The term covers a wide range of open-weight models, from compact 1–4 billion parameter models that run smoothly on a laptop, to 70B+ parameter behemoths that rival frontier cloud models but require a high-end GPU or a multi-GPU rig.
Thanks to breakthroughs in quantization (compressing model weights without major quality loss), running a genuinely capable local LLM has become accessible to anyone with a modern computer — even without a dedicated GPU.
Why Run a Local LLM?
Complete Privacy
Your prompts, documents, and conversations never leave your device. Ideal for sensitive business data, medical records, or personal projects.
Zero API Costs
No per-token charges, no monthly subscriptions. After the one-time hardware cost, running a local LLM is completely free.
Low Latency
Local inference eliminates round-trip latency to remote servers. Response times can be sub-second on a decent GPU.
Works Offline
Run your AI assistant on a plane, in a remote location, or anywhere without internet access.
Full Customization
Fine-tune models on your own data, modify system prompts, and integrate with any local tool or workflow.
No Vendor Lock-In
Switch between models freely. If a new model outperforms your current one, swap it in minutes with no cost.
Best Local LLMs in 2026
A quick-reference table of the most capable open-weight models you can run locally right now.
| Model | Size | Best For | Min RAM | License |
|---|---|---|---|---|
| Qwen3.8 27B | 27B dense | Coding, long-horizon agents, vision, and general use | 24 GB | Apache 2.0 |
| Qwen3.6 27B | 27B dense | Users prioritizing mature local packaging and known runtime behavior | 24 GB | Apache 2.0 |
| Gemma 4 31B It QAT W4a16 Ct | 31B dense | Vision, chat, RAG, and mixed workloads | 24 GB | Apache 2.0 |
| Qwen3.8 Flash Next | 176B MoE / 6B active | Large-memory workstations that prioritize capability per active parameter | 128 GB | Qwen Community 1.0 |
| GLM 5.3 Flash | 320B MoE / 18B active | Multi-GPU servers running coding, vision, and long-horizon agents | 384 GB | MIT |
| Qwen3-Coder 30B-A3B | 30.5B MoE / 3.3B active | Efficient agentic coding with mature runtimes | 24 GB | Apache 2.0 |
How to Run a Local LLM (Quick Start)
The fastest way to run a local LLM is with Ollama — a free, open-source tool that handles model download, quantization, and serving in a single command.
Install Ollama
Ollama runs on macOS, Linux, and Windows. One-line install:
curl -fsSL https://ollama.com/install.sh | shDownload a model
Pull any model from the Ollama library. Llama 3.2 is a great starting point:
ollama pull llama3.2Start chatting
Run the model interactively in your terminal:
ollama run llama3.2Use the API (optional)
Ollama exposes an OpenAI-compatible REST API on localhost:11434 for integrations.
Hardware Requirements for Local LLMs
You don't need expensive hardware to get started. Here's what to expect at each tier.
8–16 GB RAM, no GPU
CPU-only inference. Slow but works for light use.
- ✓ Qwen3 8B
- ✓ Gemma 4 E4B
- ✓ Llama 3.2 3B
16–32 GB RAM + GPU 8–20 GB VRAM
Fast inference. Handles most day-to-day tasks excellently.
- ✓ Phi-4 14B
- ✓ Gemma 4 12B
- ✓ Mistral Small 3.1 24B
32 GB+ RAM or GPU ≥20 GB VRAM
Near-frontier quality. Suitable for professional workloads.
- ✓ Qwen3.8 27B
- ✓ Qwen3.6 27B
- ✓ Gemma 4 31B
Best Tools to Run Local LLMs
Several excellent open-source tools make running local LLMs easy, regardless of your technical level.
Ollama
CLI / APIThe most popular local LLM runner. Installs in seconds, supports 100+ models, and exposes an OpenAI-compatible API.
LM Studio
Desktop GUIA polished desktop app for Mac, Windows, and Linux. Best for beginners — no terminal required.
Jan
Desktop GUIOpen-source desktop app with a clean chat UI and built-in model hub. Great alternative to LM Studio.
llama.cpp
CLI / LibraryThe engine behind most local LLM tools. Direct CPU/GPU inference with maximum control.
Can a Local LLM Replace ChatGPT?
In 2026, many developers and knowledge workers have switched from ChatGPT to running a local LLM daily. Here's the honest comparison for the most common use cases:
Task
Code assistance
Verdict
✅ Local wins
Local coding models are especially useful for private repositories, offline work, autocomplete, explanation, and repeatable workflows. The strongest choice depends on available memory and agent-tool support.
Task
Writing & editing
Verdict
✅ Local wins
Qwen3 32B and Mistral Small 3.2 24B produce publication-quality writing. For sensitive documents, drafts, and internal content, local LLMs are a direct drop-in.
Task
Document summarization
Verdict
✅ Local wins
Summarizing PDFs, contracts, or research papers locally is one of the strongest privacy use cases. Long documents still require enough memory for the selected context length and runtime cache.
Task
General Q&A
Verdict
~ Roughly equal
For factual questions about stable knowledge, top 14B+ local models are indistinguishable from ChatGPT. For very recent events, cloud AI has an edge (web access).
Task
Real-time web search
Verdict
❌ Cloud wins
ChatGPT and Claude have live web search. Local LLMs are offline by default — though you can add retrieval tools via Open WebUI or Ollama integrations.
Task
Complex reasoning
Verdict
~ Getting close
Strong local reasoning models can handle many coding, math, and analytical tasks, but the hardest agent workflows still benefit from larger models, better tools, and careful evaluation.
Bottom line:
For coding, writing, summarizing documents, and general Q&A, a well-chosen local LLM can absolutely replace your ChatGPT subscription in 2026 — and do it privately, offline, and for free. The main cases where cloud AI still wins: real-time web search, image generation, and the most complex multi-step reasoning chains.
Frequently Asked Questions
What is the best local LLM in 2026?
There is no universal winner. The current LocalAIRun ranking favors Qwen3.8 27B as the practical overall pick, with Qwen3.6 as a mature compatibility option, Gemma 4 for multimodal work, and the new Qwen3.8 Flash Next or GLM 5.3 Flash for much larger workstation and server deployments.
Can I run a local LLM on Mac?
Yes. Apple Silicon unified memory works well for local models, but the usable size depends on total memory, quantization, context length, and runtime. Start with the hardware-fit calculator using your exact memory configuration.
What is the best local LLM for most people?
Qwen3 8B is an accessible starting point for chat and light coding. Users with roughly 24–32 GB available memory can consider stronger models such as Qwen3.8 27B, Qwen3.6 27B, or Gemma 4 31B.
Do I need a GPU to run a local LLM?
No. Many models run on CPU-only systems, though they are slower. A dedicated GPU with 8+ GB VRAM dramatically speeds up inference and enables larger models. Apple Silicon Macs are especially efficient thanks to their unified memory architecture.
Is a local LLM private and offline?
Yes, completely. A local LLM runs entirely on your hardware — no data is sent anywhere. It works offline with no internet connection required after the initial model download. This makes local LLMs ideal for sensitive data, code, and private documents.
Local LLM vs Claude: which is better?
Cloud models usually remain easier and more reliable for the hardest managed agent workflows. Local models are stronger when privacy, offline use, predictable cost, model control, or repeatable on-device workflows matter.
How much does it cost to run a local LLM?
The software is completely free. You only pay for hardware — which you may already own. Running a local LLM on a MacBook or GPU workstation costs nothing beyond electricity (cents per day for typical use).
Running a Local LLM on Mac
Apple Silicon Macs (M1, M2, M3, M4) are the best laptops and desktops for local LLMs. The unified memory architecture means GPU and CPU share the same RAM pool, letting you run much larger models than a discrete GPU of the same memory size.
M1 / M2 (8–16 GB)
Qwen3 8B or Phi-4 14B
Start with Qwen3 8B on smaller systems. A 16 GB Mac can try Phi-4 14B with limited context and enough operating-system headroom.
ollama run qwen3:8bM3 Pro / M2 Pro (18–36 GB)
Mistral Small 3.1 24B or Gemma 4 31B
This memory tier can run larger quantized models, but artifact format and context cache determine whether the setup remains comfortable.
ollama run mistral-small:24bM3 Max / M4 Max (48–128 GB)
Qwen3.8 27B, Qwen3.6 27B, or Gemma 4 31B
Large unified-memory Macs can run stronger quantized models, but actual speed and context headroom depend on the runtime and artifact format.
ollama run qwen3-coder:30bMac Mini M4 Pro
Compact unified-memory desktop
Choose the memory configuration first. Larger-memory variants can run 27B–31B quantized models, while lower-memory variants should start with 8B–14B models.
ollama run qwen3:8bOllama natively supports Apple Metal GPU on all M-series chips — no configuration needed. Full macOS setup guide →
Local LLM vs Claude, ChatGPT, and Cloud AI
In 2026, the choice between a local LLM and cloud AI is less about capability and more about priorities.
| Factor | Local LLM | Claude / ChatGPT |
|---|---|---|
| Cost | ✅ Free (hardware you own) | ❌ $20/month subscription |
| Offline use | ✅ Works without internet | ❌ Requires internet |
| Raw capability | ~ Close with 70B models | ✅ Still slightly ahead |
| Privacy | ✅ 100% local, no data sent | ❌ Data sent to servers |
| Cost | ✅ Free (hardware you own) | ❌ $20/month subscription |
| Offline use | ✅ Works without internet | ❌ Requires internet |
| Speed (GPU) | ✅ 50–100 tok/s on good GPU | ~ 50–80 tok/s varies |
| Customization | ✅ Fine-tune, modify freely | ❌ Fixed model, no control |
| Vision / multimodal | ~ Gemma 3, LLaVA | ✅ Better vision models |
For private, offline, or cost-sensitive workloads, local LLMs win. For the absolute frontier of capability, Claude 5 Opus and GPT-4.5 still lead.
Find a local AI setup that fits
Start with what you want to do, or enter the hardware you already own.
Already chose a model? Open the Ollama setup guide →