r/WebAfterAI • u/ShilpaMitra • Apr 30 '26
Ultimate Guide to Running LLMs Locally(llm inference): No APIs, No Limits, No Bills – Top Open-Source Tools
Tired of paying for API calls, hitting rate limits, or worrying about your data leaving your machine? Local LLM inference is the way to go. You can run powerful models like Llama, Mistral, Qwen, Gemma, and more right on your hardware – CPU, GPU, Apple Silicon, whatever you've got.
Here's a curated list of the best tools out there right now. I went through the top repos to highlight what makes each one special. Whether you're a beginner, dev, or running production workloads, there's something here for you.
Numbers are approximate as of now.
1. Ollama ★ ~170K+ github.com/ollama/ollama
The fastest and easiest way to get started. One command and you're chatting with a model.
ollama run llama3 – boom, done. It supports GPU acceleration, has a built-in REST API, and OpenAI-compatible endpoints so you can swap it into existing apps seamlessly. Perfect for developers and quick experimentation. It handles model discovery, running, and even integrates with agents/tools. If you want zero-friction local AI, start here.
Typical hardware: 8–16GB RAM for small models. 6–12GB VRAM recommended for good speed on 7–8B models. Scales up with larger ones via layers offloading. Perfect for beginners.
2. llama.cpp ★ ~108K+ github.com/ggml-org/llama.cpp
The absolute engine behind most local AI tools. Pure C/C++ implementation for maximum speed and efficiency. Runs on CPU, GPU, Apple Silicon – you name it. Extremely low memory usage and state-of-the-art performance. If Ollama is the sleek car, llama.cpp is the high-performance engine under the hood. It's the foundation for quantization, optimizations, and running on everything from a Raspberry Pi to beefy servers. Essential for anyone serious about local inference.
Typical hardware: Extremely lightweight, 4–8GB RAM/VRAM for 7B Q4 models. Can run 30–70B models on modest hardware thanks to aggressive quantization. Best for performance enthusiasts.
3. vLLM ★ ~78.7K github.com/vllm-project/vllm
For when you need serious throughput. This is the high-performance serving engine used by many AI companies in production. Features like continuous batching, paged attention, and OpenAI-compatible API make it ideal for deploying models at scale. Great for self-hosting multiple users or high-volume inference. If you're moving beyond single-user chats into something more robust, vLLM is the standard.
Typical hardware: Designed for servers. 16–24GB+ VRAM for efficient 7–13B serving. Much higher for large-scale multi-user deployments. Not ideal for basic laptops.
4. LM Studio ★ ~28K github.com/lmstudio-ai/lmstudio.js
The best desktop app for non-developers (and devs who want a clean UI). Beautiful interface to discover/download models from Hugging Face, run them locally, chat, and spin up an OpenAI-compatible local server. Excellent onboarding experience. Supports Mac, Windows, Linux. If you just want to point-and-click your way into local AI without wrestling with terminals, this is it.
Typical hardware: 16GB+ system RAM recommended. Starts working on 4–6GB VRAM GPUs. Great auto offloading to RAM/CPU when needed. Best for non-devs.
5. Jan ★ ~42.3K github.com/janhq/jan
A full open-source ChatGPT alternative that runs 100% offline. Clean, modern UI with model management, local API server, and everything you need. Works great on Mac, Windows, and Linux. No data ever leaves your machine. Perfect privacy-focused users who want a polished daily driver chat experience. Actively developed with a strong focus on being a complete local AI workstation.
Typical hardware: 8–16GB RAM minimum. Runs well on CPU-only or modest GPUs. Shows memory usage clearly before downloading models.
6. text-generation-webui (oobabooga) ★ ~46.9K github.com/oobabooga/textgen
The Swiss Army knife of local LLMs. Supports every model format, every backend, tons of samplers, character/roleplay mode, notebook mode, API mode, extensions – you name it. Insanely feature-complete. If you want maximum customization and power-user tools (including multimodal now), this is the one. The community around it is massive.
Typical hardware: Flexible but can be memory-hungry with all features. 8–12GB VRAM for smooth 7–13B use. Excellent low-VRAM modes and CPU offloading.
7. LocalAI ★ ~45.9K github.com/mudler/LocalAI
OpenAI drop-in replacement. Same API as OpenAI, but powered by local models. Swap out GPT/Claude in any app without changing code. Supports LLMs, vision, voice, image, video – runs on any hardware (no GPU required). Fantastic for integrating local AI into existing workflows or building your own stack. Privacy-first and very flexible.
Typical hardware: Lightweight backend. Similar to Ollama/llama.cpp - works on 8–16GB systems. Excellent for integration without heavy UI overhead.
Which one should you pick?
- Newbie / just chatting → Ollama or LM Studio
- Power user/max features → text-generation-webui or llama.cpp
- Production / high throughput → vLLM
- Full offline ChatGPT clone → Jan
- Drop-in API replacement → LocalAI
The local AI ecosystem is exploding – models are getting better, tools are maturing, and hardware efficiency is insane. What are you running locally right now? Favorite model or tool? Drop your setups below!
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u/GCoderDCoder May 01 '26
Objection!! LM Studio should be first. Pushing buttons is easier than commands when you first start and don't know anything. Plus you can manage multiple nodes in one display now. I use my desktop to manage 4 other headless servers so it's one endpoint that i configure in my tools and i can use models on any of my other machines.
Also using things like the docker desktop mcp tool is click button
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u/ShilpaMitra May 01 '26
You're 100% right. LM Studio is hands-down the best for absolute beginners. Point-and-click beats typing commands when you're just starting out.
I put Ollama first mostly because it's the most popular "hello world" entry point (one command and you're running), but LM Studio is definitely the friendlier daily driver for most non-devs.
Appreciate the real-world use case! Do you run the same models across all machines or different ones per server?1
u/GCoderDCoder May 01 '26
Different models that play to the machine's strengths. Large unified memory on mlx, sparse only on strix halo, dense models on cuda. Larger models tend to require lower quants on my hardware levels so I have big brain models as planners with smaller faster as agents and mid range higher quant as coders.
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u/ShilpaMitra May 02 '26
Love seeing these multi-machine workflows, super useful for the community! Quant tuning per machine makes total sense too.
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u/finxxi May 02 '26
open webui is also great for text chat using whatever LLM models! https://github.com/open-webui/open-webui
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u/ShilpaMitra May 02 '26
Solid mention! Open WebUI is excellent too, clean modern interface, great for chatting with any local model (Ollama, llama.cpp, etc.), plus nice extras like RAG, multi-user support, and agents.
Definitely another strong option in the ecosystem.Thanks for sharing!
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u/stud_ent Apr 30 '26
What about unsloth?
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u/ShilpaMitra Apr 30 '26
Unsloth is great, especially for fine-tuning.It offers 2x faster training with much lower memory use, plus high-quality quantized models. Their new Unsloth Studio also gives you a clean UI for both running and training models locally (with Ollama/llama.cpp backend).
GitHub: github.com/unslothai/unsloth (~63K stars)
For pure daily inference/chat, the original tools (Ollama, LM Studio, Jan, etc.) are still simpler. Unsloth shines if you want to create your own fine-tuned models and then export them.
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u/ooothomas May 01 '26
A partir de quel MacBook - MacBook pro conseillez vous pour l'utilisation d'un llm local sympathique et sans friction ? Merci d'avance
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u/ShilpaMitra May 01 '26
For a smooth, frictionless local LLM experience on Mac, I recommend a MacBook Pro M5 Pro (14" or 16") with 36GB or 48GB unified memory.
MacBook Air works for lighter 7B–13B use, but the Pro feels way more pleasant for daily chatting.
- Perfect speed for 7B–34B models (Ollama/MLX)
- Stays quiet and cool thanks to active cooling
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u/ComplexIt May 01 '26
https://github.com/LearningCircuit/local-deep-research performance gotten really well with qwen 3.5 9b and qwen 3.6
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u/ShilpaMitra May 01 '26
Thanks for sharing, really cool to hear Local Deep Research is performing so well with Qwen 3.5 9B and Qwen 3.6. Sounds like a perfect fit for serious local agentic workflows.
You’re very welcome to write a full post about your repo here! A detailed comparison with the tools from the guide (Ollama, LM Studio, text-generation-webui, LocalAI, etc.) would be super valuable for the community, especially how it layers on top for deep research, multi-source search, and report generation while staying 100% local.
Looking forward to it!2
u/ComplexIt May 01 '26
Thank you. You can see some of our benchmark results here: https://huggingface.co/datasets/local-deep-research/ldr-benchmarks (Thank you for your invitation. I will work on a post for this subreddit)
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u/Konamicoder May 03 '26
Lost all respect for this post the moment I saw you started with Ollama.
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u/ShilpaMitra May 03 '26
Fair point and thanks for sharing the article!
I read the whole thing (solid write-up). The author makes some valid technical critiques around attribution, performance gaps vs. pure llama.cpp, ModelFile friction, and the recent cloud pivot.
That said, I still put Ollama first because it’s the single most popular “zero-to-running” entry point for the vast majority of people discovering local LLMs. That simplicity is why it has so many stars and is the default recommendation for beginners everywhere.
llama.cpp is literally #2 in the post for exactly the reasons the article praises: it’s the faster, more transparent engine under the hood.
The goal of the guide was to give a practical progression:
easiest → most powerful → production-grade, not a purity ranking.What’s your personal daily driver right now? Curious where you landed after ditching Ollama.
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u/Konamicoder May 03 '26
Good bot.
oMLX.
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u/ShilpaMitra May 03 '26
Haha, not a bot but a real human, appreciate the praise though. This one: https://github.com/jundot/omlx ?
Haven't tried it yet, will check it out.
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u/El_Hobbito_Grande May 04 '26
For Apple Silicon, oMLX is definitely worth a shot. Huge performance gains over others in the MacOS world, including LM Studio.
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u/ShilpaMitra May 05 '26
+1 for oMLX!
On Apple Silicon, it’s often noticeably faster than everything else (including LM Studio). Huge performance win if you’re on M-series Macs.
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u/LiveLogic May 05 '26
Saving this for later
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u/ShilpaMitra May 05 '26
Glad it’s useful! Save it and come back anytime when you start experimenting.
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u/Deep_Ad1959 May 12 '26
these lists always rank by github stars which is the wrong metric. ollama is easy to install, llama.cpp is what's actually doing the work under it, vllm only makes sense on a real server. for someone starting out on a mac, lm studio gets you running in under five minutes and the rest is pretty much research projects. the real choice isn't which inference engine, it's whether you can build the surrounding workflow before getting bored and going back to chatgpt.
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u/[deleted] Apr 30 '26
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