How to Run a Large LLM Locally (32B, 70B & 120B): Quantization, Hybrid Offload & Plug-and-Play RAM Pooling
Running a large LLM locally—defined as 30B, 70B, or 120B+ parameters—is fundamentally a memory capacity problem rather than a compute problem. Because unquantized 70B weights require ~140 GB of memory (far exceeding a 24 GB GPU), you can run large LLMs locally using three proven methods: (1) 4-Bit Quantization (Q4_K_M / GGUF) to shrink a 70B model to ~42 GB; (2) Single-PC Hybrid GPU/CPU Offload; or (3) Plug-and-Play Network Memory Pooling using a consumer appliance like the RAMDeck Hardware Hub ($159 Indiegogo Special + Free $29 Zero-Install Node USB) to combine the RAM and GPUs of multiple computers and phones into one 48 GB to 140 GB+ local AI cluster.
Standard consumer graphics cards hit a hard VRAM ceiling at 8 GB, 12 GB, 16 GB, or 24 GB. When you attempt to load a 32B, 70B, or 120B parameter model into a single GPU that does not have enough memory, your inference engine immediately crashes with an Out-of-Memory (OOM) error—or grinds to a halt trying to swap dense weights from disk.
Below is the complete guide to the three strategies to bypass single-GPU VRAM limits, along with verified memory sizing tables and live multi-device video benchmarks.
1. Shrink the Model (Aggressive GGUF Quantization)
FP16) model weights unless you own a multi-GPU server rack. Converting or downloading models in quantized .gguf format compresses each parameter from 16 bits down to 4 bits (or 2–3 bits) with minimal perplexity loss.
Q4_K_M(4-Bit Medium — The Gold Standard): Shrinks a 30B/32B model from ~64 GB down to ~17–20 GB, and shrinks a 70B model from ~140 GB down to ~40–43 GB while retaining ~99% of original reasoning and coding accuracy.IQ3_XXS/Q3_K_M(3-Bit Extreme Compression): Squeezes a 70B model into ~28–31 GB so it can fit across two 16 GB computers.IQ2_M/Q2_K(2-Bit Emergency Fit): Compresses a 70B model down to ~22–24 GB, though reasoning quality noticeably degrades compared to 4-bit weights running across pooled RAM.
2. Split Across a Single PC (Hybrid VRAM/RAM Offload)
- Single-PC Hybrid GPU + System RAM Split: Desktop apps like LM Studio, Ollama, and Jan let you place as many transformer layers as possible inside your fast GPU VRAM and spill the remaining layers into motherboard system RAM. However, if your single PC only has 16 GB or 32 GB of system RAM, a 70B model (~42 GB) or 120B model (~65 GB) will still crash your PC.
- Sparse MoE Acceleration: Unlike dense models (which activate 100% of parameters on every token), sparse Mixture-of-Experts (MoE) architectures activate only a small fraction of experts per token. In our verified benchmark with Qwen 3.8 Flash Next (66.6 GB Sparse MoE), RAMDeck’s standalone SSD mode achieved 6.89–8.79 tokens per second and loaded in under 60 seconds (watch the benchmark video).
3. Pool RAM Across Multiple Computers & Phones (Distributed Network Inference)
Because Large Language Models compute sequentially layer by layer, Machine #1 can process Layers 1–25 in its own RAM, send a compact kilobyte-sized activation vector over your home router to Machine #2 for Layers 26–55, and finish Layers 56–80 on Machine #3. You have two ways to set up network memory pooling:
Option A: 100% Plug-and-Play Hardware Hub + Zero-Install Node USB (RAMDeck)
Built for everyday users, builders, and teams who want a 60-second setup with zero coding, the RAMDeck Hardware Hub ($159 Indiegogo Special) plugs directly into your router and unites your devices automatically:
- Included Free Zero-Install Node USB ($29 Retail Value): Plug the Official RAMDeck Node USB into any Mac, Windows PC, or Linux machine and double-click
Run_RAMDeck_Mac,Run_RAMDeck_Windows, orRun_RAMDeck_Linux. It pools that computer’s RAM and GPU into your cluster in seconds with zero installation and zero internet downloads. Better yet, once launched into temporary memory, you can physically unplug the USB stick (“Unplug & Walk”) and walk around your house or office to activate Computer #2, Computer #3, and Computer #4 with that same single thumb drive! PressCtrl+Cwhen done and zero files remain behind. - True 5-OS Cross-Platform Pooling: Combine Windows, macOS, Linux, iOS (iPhone & iPad via Apple App Store), and Android (Google Play) into one cluster.
Every $159 RAMDeck Hub Includes 1 Official RAMDeck Node USB FREE
After our Indiegogo campaign concludes, the Official RAMDeck Node USB will retail separately for $29. Back the RAMDeck Hardware Hub ($159 Indiegogo Special) during the campaign and receive one (1) RAMDeck Node USB per Hub completely FREE—automatically included for both new and existing backers!
Claim Your $159 RAMDeck Hub + Free $29 Node USB on Indiegogo →Option B: Comparing Other Tools (Exo vs. ExLlamaV2 vs. Petals)
Depending on your hardware and comfort with command-line terminals, here is how other popular local AI tools compare:
- Free Open-Source CLI Tools (Exo & Manual Scripts): Built for software engineers who want to spend time in command-line terminals managing developer environments across their machines. See our comparison in RAMDeck vs. Free Open-Source CLI Tools.
- ExLlamaV2: Designed for single-PC workstations with multiple NVIDIA GPUs installed on the same motherboard; it cannot pool separate laptops, Macs, or phones over a home network.
- Petals: A public internet swarm where your prompts are routed across unknown third-party computers online rather than staying private on your home network.
What Size LLM Can You Actually Run? (Memory Sizing & Verified Speeds)
Use this empirical sizing table to match your total memory (either on one machine or pooled across multiple devices on your network) to the exact model class and verified RAMDeck benchmark speed:
| Total Pooled / System Memory | Max Recommended Model Size (4-Bit Q4_K_M) |
Verified RAMDeck Cluster Example & Speed |
|---|---|---|
| 16 GB – 24 GB | 13B – 14B Dense or 30B Sparse MoE (~17–18 GB) |
• 38–39 tok/s on Qwen3-Coder 30B MoE (RTX 3060 Mini PC + Mac mini M4 — Watch) • 28 tok/s on Qwen 30B MoE (M3 MacBook + iPhone 16 + iPhone XS — Watch) • 11–19 tok/s on Qwen3.5 13B Dense (2019 Acer + Mini PC + Mac mini — Watch) |
| 24 GB – 32 GB | 27B – 32B Dense or 35B – 40B MoE (~20–26 GB) |
• ~29 tok/s on Qwen 35B Q5 (~26 GB across RTX 3060 + Mac mini M4 — Watch) • 16 tok/s on REAP 40B MoE (25+ GB across Mini PC + Mac mini + Acer — Watch) • 1.92 tok/s on Qwen3.8 27B Dense (2019 Acer Primary + Mini PC + Mac mini + S24+ — Watch) |
| 48 GB – 64 GB | 70B Dense (Llama 3.3 70B / DeepSeek-R1 70B, ~42–44 GB) or 66GB Sparse MoE |
• Three 16 GB computers pool into 48 GB (~42 GB usable) to run 70B models without Out-of-Memory crashes (70B Guide) • 6.89–8.79 tok/s on 66.6 GB Qwen 3.8 Flash Sparse MoE (Watch) |
| 96 GB – 148 GB+ | 120B+ Frontier Models (60 GB+ weights + large context) |
• 3.1 tok/s on 60 GB+ 120B Model across 5 computers (2 Macs + 2 Windows PCs + 1 Ubuntu Linux Mini PC, ~92 GB usable — Watch) • 1.37 tok/s on gpt-oss-120b across 6 mixed devices during 40-hour chaos test (Watch)
|
How to Reuse RAM from an Old PC to Run AI
Read our full step-by-step tutorial on combining old computers in How to Pool RAM from Old PCs: The Complete Guide and How to Use an Old PC for Local AI.
Frequently Asked Questions
How do I run a 70B LLM locally without a $2,000 GPU?
Download the 4-bit quantized GGUF version (Q4_K_M, ~42 GB) and pool the memory of three 16 GB computers over your local network using a plug-and-play RAMDeck Hardware Hub ($159 Indiegogo Special) or distributed inference software. Together, three 16 GB machines provide 48 GB of total memory (~42 GB usable), holding the entire 70B model in RAM.
Why does a 30B MoE model run at 30–39 tok/s while a 27B Dense model runs at 1.92 tok/s?
A sparse Mixture-of-Experts (MoE) model like Qwen3-Coder 30B (A3B) holds 30 billion parameters in RAM but only computes 3 billion active parameters per token—allowing pooled GPUs and Apple Silicon Macs to generate at 28 to 39 tokens per second. A 27B Dense model activates all 27 billion parameters on every single token, and when anchored on a 2019 CPU-only laptop, trades raw speed (1.92 tok/s) for the ability to run a model that would otherwise crash.
Can I combine RAM across a MacBook, a Windows PC, a Linux box, and an iPhone?
Yes. RAMDeck supports Windows, macOS, Linux, iOS (via the official Apple App Store app), and Android (via Google Play) simultaneously in the same cluster, plus zero-install activation via the included RAMDeck Node USB.
Run 30B, 70B & 120B Models on the Hardware You Already Own
Back the plug-and-play RAMDeck Hardware Hub for $159 (Indiegogo Special) and get one Official RAMDeck Node USB ($29 retail value) included completely FREE in the box with every Hub.
Back RAMDeck on Indiegogo ($159 + Free $29 Node USB) →