Can LM Studio or Ollama Split an LLM Across Multiple Computers or Devices?
Inside one computer: Yes. Across two separate computers, Macs, or phones: No. LM Studio and Ollama can split a model’s layers across components plugged into one single motherboard—either dividing weights between your GPU VRAM and CPU System RAM (GPU Offload slider) or spanning multiple PCIe graphics cards inside the same desktop tower. Neither app can pool RAM across two separate laptops, Macs, PCs, or smartphones over Wi-Fi or Ethernet. To split 30B, 70B, and 120B+ local LLMs across multiple separate machines with zero terminal coding, use the plug-and-play RAMDeck Hardware Hub ($199).
1. What LM Studio & Ollama Can Split Inside One Single Computer
Much of the confusion online comes from the word “device.” In graphics-driver terminology, your CPU and your GPU inside the same PC are each called a “compute device.” Here are the two ways single-PC apps split a model internally:
A. Hybrid GPU VRAM + CPU System RAM Offload (The “GPU Offload” Slider)
If you have an 8 GB or 12 GB graphics card and 32 GB of motherboard DDR4/DDR5 RAM inside one PC, LM Studio’s GPU Offload slider (and Ollama’s automatic layer split) places as many neural-network layers as possible onto your fast GPU VRAM and spills the leftover layers into that same PC’s slower CPU System RAM.
B. Multi-GPU Inside One Desktop Motherboard (PCIe Tensor Split)
If you own a large desktop tower with two NVIDIA or AMD graphics cards physically slotted into the same motherboard’s PCIe slots (for example, two 12 GB cards totaling 24 GB VRAM), LM Studio and Ollama can distribute layers across both cards inside that single chassis.
2. What LM Studio & Ollama Cannot Do Across Separate Machines
- Cannot Pool Two Separate Laptops, PCs, or Macs: If you have a 16 GB Windows laptop and a 16 GB MacBook sitting side by side on your desk, LM Studio and Ollama cannot combine them into a 32 GB pool. Each machine remains strictly trapped behind its own single-motherboard memory ceiling.
- “Start Server” &
ollama serveShare a Chat Link—Not RAM: Turning on Local Server mode in LM Studio or Ollama loads 100% of the model into Computer #1’s memory and merely broadcasts an HTTP chat endpoint on your Wi-Fi so Computer #2 can type prompts into a browser. Computer #2 contributes 0 MB of RAM and 0% of its GPU. - The Single-PC CPU Spillover Speed Cliff (3–7 tok/s): Dedicated GPU VRAM runs at 300 to 1,000+ GB/s, while dual-channel desktop DDR4/DDR5 CPU RAM runs at only 30 to 80 GB/s. The moment LM Studio spills half of a 30B or 70B model onto single-PC CPU RAM, every generated token stalls waiting on that narrow CPU bus—dropping speed from conversational streaming down to 1–7 tokens/sec (or below 0.1 tok/s if the model exceeds total RAM and hits SSD virtual-memory swap).
3. How to Split an LLM Across Separate Computers, Macs & Phones Over a Network
Instead of spilling excess layers onto slow single-PC CPU RAM—or spending $3,000+ on a dual-GPU tower—multi-device network layer splitting lets Computer #1 keep its GPU full and hand the remaining layers across Ethernet, Wi-Fi, or Tailscale straight into Computer #2’s GPU or Apple Silicon Unified Memory. Today there are two ways to do it:
| Capability | LM Studio / Ollama (Single PC) | Open-Source Developer CLI (Exo / Raw RPC) | RAMDeck Hardware Hub (Plug-and-Play Appliance) |
|---|---|---|---|
| Splits Across CPU + GPU in 1 PC? | Yes (GPU Offload slider) |
Yes | Yes (Automatic) |
| Splits Across Separate PCs, Macs & Phones? | No (Capped at 1 motherboard) | Yes (Manual terminal setup) | Yes — Pool 2 to 6+ Devices (48–140 GB+) |
| Setup Skill Required | Point-and-click (1 PC only) | Programmers comfortable in command-line terminals | Zero Coding — Plug Hub into Router & Click Connect (60s) |
| Supported Operating Systems | Win / Mac / Linux (Isolated) | Primarily Mac / Linux developer setups | Windows + macOS + Linux + iOS App Store + Android Together |
| Zero-Install Portable USB Activation | No | No | Included FREE ($29 Value) — “Unplug & Walk” Across PCs |
| Remote GPU Pooling Over Internet | No | Manual VPN networking | Built-In 1-Click Tailscale Mesh (31 tok/s Verified) |
| 30B MoE Speed on 12 GB GPU Overflow | ~3–7 tok/s (CPU RAM spillover) or OOM | Varies by manual tuning | 38–39 tok/s (RTX 3060 Mini PC + M4 Mac mini) |
4. Keep Your Favorite Chat UI & Coding Tools While RAMDeck Pools the Memory Underneath
Switching from single-PC apps to RAMDeck does not mean giving up a clean ChatGPT-style interface or developer plugins:
- Built-In Multi-User Browser Dashboard + Private PDF Vault (RAG): Open
http://ramdeck.local:8420from any laptop, tablet, or phone in your building to chat with 30B, 70B, and 120B+ models and query confidential PDFs with exact source citations—supporting strict air-gapped workflows (including ABA Formal Opinion 512 for legal practices). - Drop-In OpenAI-Compatible Local API: Point
Open-WebUI,Continue.dev,Cline,VS Code, orCursorstraight at your RAMDeck Hub endpoint. Your favorite frontend sees one unified server while RAMDeck silently coordinates the RAM and GPUs of 2 to 6 machines behind the scenes. - Zero-Install “Unplug & Walk” Node USB: Every RAMDeck Hub includes 1 Free Official RAMDeck Node USB ($29 retail value). Plug it into a family laptop or spare office PC, double-click once to stage the contributor in temporary memory, physically unplug the USB drive, and walk it over to activate your next computer—leaving 0 bytes installed on host drives.
Have a soldered 16 GB Apple Silicon Mac or spare DDR4 laptops you want to combine? Read our companion guide: How to Reuse & Combine Existing RAM for Local LLMs (Including Soldered M1, M2, M3 & M4 Macs), or test your exact device mix in the Interactive Local LLM RAM Calculator.
Frequently Asked Questions
Can LM Studio split a 70B LLM across two 32 GB computers?
No. LM Studio only sees the RAM and GPUs inside the single computer where it is running. To combine two 32 GB computers (or three 16 GB computers) into a unified 48 GB–64 GB memory pool capable of holding a 42 GB 4-bit 70B model, connect them over Ethernet or Wi-Fi with the RAMDeck Hardware Hub.
Why is network GPU pooling faster than LM Studio’s CPU RAM offload on 30B models?
When a 20 GB 30B model overflows a 12 GB GPU inside one PC, LM Studio offloads the remaining 8 GB onto slow motherboard CPU RAM (~30–80 GB/s), dragging speed down to 3–7 tok/s. With RAMDeck, only tiny kilobyte-sized activation signals cross your network per token (~0.13 ms on Gigabit Ethernet) while the overflow weights sit inside a second machine’s fast GPU or Apple Silicon Unified Memory—sustaining 28 to 39 tokens/sec.
Can I combine a Windows gaming PC with an Apple Silicon MacBook and iPhones?
Yes. RAMDeck unites NVIDIA CUDA GPUs on Windows/Linux, Metal Unified Memory on M1–M4 Macs, x86 CPU system RAM, and official iOS/Android mobile contributors (with an iPhone 16 contributing ~3.2 GB of active unified memory) in one plug-and-play cluster.
Break Past Single-PC Memory Limits in 60 Seconds
Order the plug-and-play RAMDeck Hardware Hub ($199 • 134% funded on Indiegogo • ships in January with 1 FREE $29 Official Zero-Install Node USB in every box), or read our full 10-chapter book Pool the RAM You Already Own (ISBN 979-8-16746-277-9) free at ramdeck.com/book.
— The TradeMAV Team • Trademark Notice: TradeMAV™ is a trademark of TradeMAV LLC. All third-party product names, including LM Studio, Ollama, Exo, Open-WebUI, Continue.dev, VS Code, Cursor, and Tailscale, are trademarks or project names of their respective owners, which are not affiliated with, sponsoring, or endorsing RAMDeck or TradeMAV LLC.