What Is a Local LLM & How to Run One: LM Studio vs. Ollama vs. Multi-Device RAM Pooling (RAMDeck)

1. Why Run an LLM Locally Instead of Cloud AI?

• Privacy & Zero Data Egress: Your prompts, source code, contracts, and financial or client documents never leave your hardware—supporting strict on-premise confidentiality standards (such as ABA Formal Opinion 512 for legal teams).
• No Recurring Subscriptions or Token Bills: Eliminate $20 to $200/month cloud chat plans and unpredictable per-token API charges once models run on devices you already own.
• 100% Offline Capability: Chat, summarize PDFs, and run coding assistants with zero internet connection—plus optional built-in Tailscale mesh access when you want encrypted remote access from outside the house.
• Full Model & Knowledge Control: Run any open-weight GGUF model (Llama, Qwen, DeepSeek, Gemma, Mistral), index private local document vaults (RAG), or power IDE coding agents (Continue.dev, VS Code, Cursor) without vendor rate limits or silent model changes.

2. Popular Local LLM Software Runtimes, Interfaces & Hardware Hubs

Local AI platforms fall into four clear categories depending on whether you are running a small model on a single computer or pooling memory across multiple devices to run larger models:

3. Top Open-Weights Local LLM Families (Qwen, Gemma 4, Llama 3.x & DeepSeek)

Modern open-weight model families cover every hardware footprint—from pocket-sized mobile assistants to multi-device coding and reasoning engines:

• Qwen Series (Alibaba): Spans lightweight 2B and 4B models for phones and laptops up to coding powerhouses like Qwen 27B, Qwen3-Coder 30B-A3B MoE (~17–22 GB), and 32B–72B dense models.
• Gemma 4 (Google): Known for strong reasoning-per-gigabyte ratios, widely deployed in 12B (~9 GB) and 26B (~17–20 GB) sizes for local developer workflows and private document analysis.
• Llama 3.x (Meta): The community benchmark across 3B (edge), 8B (single 16 GB laptop), and Llama 3.3 70B (~40–48 GB pooled or workstation memory).
• DeepSeek-R1 / V4 Series: Reasoning-first architectures (available from distilled 8B/14B/32B variants up to 70B+) built for step-by-step math, code debugging, and autonomous local agents.

4. Hardware Sizing Guide: 1B to 120B+ Parameters & the 8 GB / 16 GB / 32 GB / 64 GB+ Tiers

A 4-bit quantized GGUF model (Q4_K_M) requires roughly 0.6× to 0.75× its parameter count in gigabytes (GB) of available RAM or VRAM (about ~1 GB per billion parameters once you include your context window / KV cache and operating system overhead):

• Minimum Tier — 8 GB RAM: Runs tiny 1B–3B models (~1.5–3 GB) smoothly for mobile text completion and basic chat; 7B–8B models feel cramped once Windows or macOS takes 3–4 GB.
• Everyday Sweet Spot — 16 GB RAM (or 8–12 GB GPU): Comfortably runs 7B–8B models (~6–8 GB) and quantized 12B models for everyday chat, summarization, and local RAG on one laptop.
• Enthusiast Tier — 32 GB RAM (or 16–24 GB GPU — OR Two Pooled 16 GB Computers): Unlocks 14B, 26B, and 30B–35B MoE models (~10–24 GB) for serious multi-file coding and complex reasoning—either on one 32 GB machine or by combining two 16 GB laptops/Macs over your network with RAMDeck.
• Power User & Professional Tier — 64 GB to 128 GB+ (Single $3,000+ Workstation OR Pooled Home Cluster): Required for 70B dense models (~40–48 GB) and 120B+ frontier models (~65–90+ GB) with large context windows. Instead of paying inflated DDR5 prices or buying a dedicated $3,000–$4,500 workstation, pooling 3 to 5 existing laptops, PCs, Macs, and phones via the RAMDeck Hardware Hub ($199) gives you 48 GB to 140 GB+ of shared physical memory for $0 in new RAM sticks.

Model Tier (4-Bit Q4_K_M)	Required Usable Memory	Single-Machine Requirement	Multi-Device Pooled Alternative (RAMDeck) & Verified Speed
1B – 3B (Llama 3.2 3B, Qwen 3B)	~2 – 3.5 GB	Any 8 GB laptop or phone	Runs standalone on any single device (40+ tok/s)
7B – 8B (Llama 3.1 8B, Gemma 2 9B)	~6 – 8.5 GB	16 GB RAM PC or 8 GB GPU	43–44 tok/s on Qwen2.5-Coder 7B across laptop + Mini PC (Watch)
13B – 14B (Qwen 14B, Phi-4)	~10 – 13 GB	32 GB RAM PC or 12 GB+ GPU	11–19 tok/s across 2019 laptop + Mini PC + Mac mini (Watch)
30B – 35B MoE (Qwen 30B A3B)	~18 – 26 GB	32–64 GB workstation or 24 GB GPU	28–39 tok/s across M3 MacBook + 2 iPhones (28 tok/s), RTX 3060 + Mac mini (39 tok/s), or Tailscale Remote GPU (31 tok/s)
70B – 72B Dense (Llama 3.3 70B, Qwen 2.5 72B)	~40 – 48 GB	Dual 24 GB GPUs or 64 GB+ Mac Studio ($3,000+)	Three 16 GB PCs/Macs pool 48 GB (~42 GB usable) for $0 in new RAM sticks (70B Blueprint)
120B+ Frontier (60 GB+ weights)	~65 – 92 GB+	128 GB workstation ($4,000+)	3.1 tok/s across 5 pooled Macs, Windows PCs & Linux Mini PC (Watch)

Want exact memory math for your specific laptops, PCs, and context window? Try our interactive Local LLM RAM Calculator.

5. Step-by-Step: The 3 Methods to Run a Local LLM

Choose your setup method based on whether your target model fits inside a single computer’s memory (Methods 1 & 2) or requires combining memory across multiple devices (Method 3):

6. Solving the 70B+ Local LLM Memory Challenge (40–48 GB Without Buying a $3,000 Workstation)

To run a truly large local LLM—such as Llama 3.3 70B, Qwen 2.5 72B, DeepSeek-R1 70B, or Mistral Large—the entire quantized model must stay resident in real physical RAM to generate at a usable speed. At 4-bit quantization (Q4_K_M), a 70B model requires 40 GB to 48 GB of available memory plus room for your context window.

• Why NVMe SSD Virtual Memory Swap Fails (<0.1 tok/s): Unlike games that load textures once, an LLM reads every active weight from memory on every single generated word. Letting a 42 GB model spill onto an NVMe SSD via OS pagefile swap thrashes flash storage below 0.1 tokens/sec and rapidly wears down SSD write endurance.
• How Multi-Device RAM Pooling Breaks the 48 GB Wall (Even on Soldered Apple Silicon Macs): Most households and small offices already own 48 GB to 96 GB+ of physical memory spread across a primary laptop, a spare desktop or Mini PC, an M1–M4 Mac, and two smartphones. Inside a RAMDeck cluster, each device keeps its assigned slice of model weights parked in fast physical RAM and exchanges only tiny kilobyte-sized handoff signals across your router—turning an Out-of-Memory crash into a working, 100% private local AI supercomputer. Read our full deep dives in How to Reuse & Combine Existing RAM for Local LLMs (Including Soldered M1–M4 Macs), Can LM Studio or Ollama Split an LLM Across Multiple Computers?, How to Run a Large LLM Locally (32B, 70B & 120B), and Distributed AI on Consumer Hardware: Ollama vs. Exo vs. RAMDeck.

Frequently Asked Questions

— The TradeMAV Team • Trademark Notice: TradeMAV™ is a trademark of TradeMAV LLC. All third-party product names, including LM Studio, Ollama, vLLM, and llama.cpp, are trademarks or project names of their respective owners, which are not affiliated with, sponsoring, or endorsing RAMDeck or TradeMAV LLC.