Local AI is the difference between renting intelligence and owning it
When you host models on your machine, you gain privacy, speed, and offline reliability
This guide shows what your PC or Mac can realistically run, the tools you'll use, and the safe way to reach first inference without getting yelled at by drivers
Why host your AI?
Privacy means prompts and documents stay on your disk
Cost control means one-time downloads beat per-token bills for casual use
Latency is local: your PCIe bus is faster than someone else's data center
For builders, local models mean rapid iteration, custom pipelines, and tinkering without platform limits
“Can my PC run this?” (interactive stub)
Paste quick specs like GPU VRAM RAM CPU and free SSD space
We will soon send this to DeepSeek for a refined plan with a strict payload you can preview
For now treat this as a blueprint of what the checker will say: 8B–13B LLMs at Q4 or Q5 on mid-range GPUs
SD 1.5 quick at 512–704px and SDXL doable with careful steps
API workflows can be exposed locally via LocalAI or an OpenAI-compatible bridge
Hugging Face: your model bazaar
Most local journeys start at Hugging Face
You will pull LLMs diffusion checkpoints tokenizers and control nets from there
Prefer ready-to-use GGUF or safetensors builds
Read the model card licenses and recommended parameters before you download heroic-sized files
Visit Hugging Face Models
Wrappers & runtimes (what you actually run)
Wrappers are the runtimes and UIs that make local models usable: download load prompt and sometimes serve via an API
Ollama is the one-line model runner with sane defaults and GPU acceleration and local HTTP
LM Studio is a friendly desktop GUI to browse models choose quantization and chat locally and it can expose an OpenAI-style API
llama.cpp and koboldcpp are fast C and C plus plus backends for GGUF-quantized LLMs across CPU GPU and Metal
LocalAI runs models behind a drop-in REST API so apps think they are talking to the cloud
ComfyUI and InvokeAI are your image pipelines for Stable Diffusion with reproducible graphs and workflow-first UIs
Download: Ollama
Choose your lane: text, image, audio, video
Text LLMs: for chat and coding 3B–13B models are the sweet spot on consumer GPUs
Use Q4 or Q5 for memory balance and keep contexts modest until RAM allows
Images Diffusion: learn on SD 1.5 then graduate to SDXL when your GPU stops sighing and manage resolution steps and ControlNets
Audio Voice: lightweight TTS and VC are realistic on gaming GPUs and latency and clean input chains matter
Video: still experimental on consumer rigs with long renders and project specific pipelines so treat it as boss level
Hardware rules of thumb (simple and honest)
LLMs quantized: 4–6GB VRAM runs 3B–7B
8–12GB runs 7B–13B
16–24GB can handle 13B–70B with trade offs
A 4070 class card feels snappy on 7B–13B chat and coding
Diffusion Stable Diffusion family: 6–8GB makes SD 1.5 comfy while SDXL is constrained
10–12GB makes SDXL 768px quite fine
16GB plus makes SDXL Turbo and heavy ControlNets practical
CPU only: fine for tiny 1–4B LLMs and low step SD mini pipelines but patience is part of the build
Starter tools you'll actually use
Ollama for CLI simplicity
LM Studio for desktop convenience and model browsing
ComfyUI and InvokeAI for diffusion workflows
LocalAI to expose a local OpenAI-style API
Install one from each lane and you are future proofed for most local projects
Links: Ollama
Realistic performance snapshots
RTX 4070 laptop can do 7B–13B LLM chat at roughly 18–35 tokens per second and coding models feel responsive
RTX 3060 desktop does SD 1.5 at 512–704px in roughly 5–12 seconds at about 20–30 steps
Apple M series machines do smaller LLMs comfortably and SD 1.5 is manageable and SDXL works with patience and good thermals
Top 10 local friendly LLM picks for average setups
Llama 3 8B Instruct is balanced with broad community support and good with Q4 or Q5 on 8–12GB VRAM
Llama 3.1 8B improves coding and chat and has a similar footprint
Mistral 7B Instruct is efficient fast and versatile as a staple for everyday chat
Gemma 7B is compact with friendly quality at small sizes and works well in Q4
Qwen 7B or 14B Instruct offers strong multilingual and reasoning with 14B wanting more VRAM
Phi family mini or medium is small and capable for quick prototypes
OpenHermes or Nous Hermes 2.5 at 7B or 8B are community fine tunes for helpful chat
MythoMax or Mythomist at 7B or 13B are instruction tuned blends that many find friendly
Code oriented 7B or 13B forks pair well with Q5 on 12GB plus VRAM
Mixtral 8x7B in quantized form offers MoE like performance if your desktop has headroom
Top 5 for low spec machines
TinyLlama 1.1B is usable for canned tasks and runs on modest CPUs and GPUs
Phi 3 mini around 3 to 4B is small and capable for note taking and lightweight chat
Qwen 2 or 2.5 small from 1.5 to 4B offers multilingual options with decent instruction following
Mistral tiny community variants at 2 to 3B are speed oriented for basic assistance
Llama variants around 3B or 4B in GGUF give minimal footprint builds for hobby rigs and older laptops
Top 5 when you have headroom
Llama 3 70B in quantized form is excellent and wants 16–24GB VRAM with careful settings
Qwen 72B in quantized form has powerful multilingual and reasoning chops but is memory heavy
Mixtral 8x7B at higher quality quants brings MoE goodness if you can feed it
Deep reasoning 30B–70B forks can do impressive reasoning without sending data to clouds
Code specialist 34B or 70B variants give longer context and stronger refactoring as long as RAM cooperates
Setup safety and sanity
Keep GPU drivers current and on Linux match CUDA or ROCm carefully
Use venv or Conda or Docker and pin versions once stable
Prefer Q4 or Q5 at small VRAM and for SDXL adjust resolution and steps before shopping for a new PSU
Disable telemetry where offered because local can still phone home if extensions are allowed
Bottlenecks and realistic upgrades
If you are VRAM bound scale down model size or quant and lower SDXL resolution and reduce ControlNets
If you are RAM bound close apps and trim context and avoid oversized checkpoints
If you are storage bound prefer NVMe because diffusion caches love fast scratch
Disclosure: We may include optional affiliate parts under Fix this bottleneck cards and we will keep it subtle and clearly labeled: As an Amazon Associate SupAI may earn commissions from qualifying purchases at no extra cost to you
Quick FAQ
Do you need the internet? Only to download models and tools and after that most workflows can run offline
Is local worse than cloud? They are different trade offs because cloud wins at massive scale while local wins for privacy cost control and experimentation speed
Will you break your laptop? You will exercise it so use a cooling pad keep vents clear and expect throttling not smoke
Bottom line
Local AI is absolutely doable on mainstream hardware if you pick the right models and settings
Start small ship something then scale your ambition or your GPU when you outgrow the floor