OpenClaw

N100 is surprisingly capable. 16GB and low power make it the best value always-on OpenClaw box.

10-core i7 and 32GB. Multiple agents and services concurrently. Fast NVMe keeps things responsive under load.

Zen 3+ with 16GB handles OpenClaw comfortably. RDNA 2 iGPU is a bonus.

Ryzen 5600GE and 16GB is a solid combo. Enterprise reliability for always-on use.

Xeon D and 32GB ECC. Run OpenClaw in a jail or VM alongside ZFS. Enterprise reliability.

12-core i5 and 16GB. Dual Ethernet makes it versatile for networking setups.

32GB unified memory. M2 Max handles multiple concurrent agents and background services effortlessly.

32GB RAM and fast CPU handle OpenClaw via cloud APIs excellently. RTX 3060 is unused by OpenClaw itself.

16GB and 6 P-cores. No dGPU means cloud-only models but everything else works great.

14-core i5-13500T in a tiny box. Silent, reliable, perfect for always-on deployment.

192GB unified memory. Can run dozens of concurrent OpenClaw agents without breaking a sweat. Way beyond what's needed.

Workstation-class i7-12700 and 32GB. ISV-certified reliability for production.

With the GPU module, runs local 7B models comfortably. Without GPU, still excellent via cloud.

Xeon E and 32GB ECC. Built for 24/7 operation. Production-grade OpenClaw deployment.

8-core Xeon D and 64GB ECC in Mini-ITX. Low power server. Perfect homelab AI host.

i5-1340P and 16GB. Silent operation. Ideal always-on OpenClaw server for cloud API workloads.

32GB RAM handles multiple agents via cloud APIs. Ryzen 5800H is fast for Node.js workloads.

32GB and Ryzen 6800U. Most RAM of any handheld. Runs multiple agents and services as a pocket server.

Zen 4 + 16GB. Runs on Windows or dual-boot Linux. Powerful CPU handles Node.js and background skills well.

Full OpenClaw with all integrations. 8GB RAM and GPU headroom for concurrent skills. Fast cold start.

16GB RAM and RK3588 make this the best ARM SBC for OpenClaw. NVMe support helps with swap. Handles multiple channels.

512GB RAM and 96+ vCPUs. Research-grade cluster. Could host hundreds of concurrent OpenClaw agents.

16GB RAM and RK3588. NVMe support helps with swap under load. Handles multiple messaging channels comfortably.

128GB RAM and fast vCPUs. Enterprise-scale concurrent agent deployment. $2500/mo is steep for just OpenClaw.

16GB RAM handles OpenClaw via cloud APIs. T4 GPU is unused by OpenClaw but available for other workloads.

8GB cloud instance is the comfortable sweet spot. All cloud API features work flawlessly.

Purpose-built appliance. Pre-configured OpenClaw with all integrations. Plug and play.

Zen 4 with 16GB. Good Linux support. RDNA 3 iGPU helps with some inference.

Runs beautifully. Near-instant API responses. 16GB handles 5-10 concurrent messaging channels without swapping.

Linux-first with 16GB. Pop!_OS and Coreboot make it the purist's OpenClaw laptop.

24GB unified memory. Multiple agents and background services simultaneously with headroom to spare.

32GB RAM and 14-core i7 handle OpenClaw and all background services. GPU unused by OpenClaw but nice for other workloads.

Fast i7-1365U and 16GB. Background OpenClaw while you work. Enterprise build quality.

M3 Pro with 18GB. Battery lasts all day even with agents running. Fast SSD keeps background tasks responsive.

Fanless M2 with 16GB. Silent always-on operation. Handles OpenClaw with all integrations enabled.

12-core Xeon and 64GB. Data center grade. Runs multiple OpenClaw instances concurrently.

Enterprise GPU cloud. 48GB RAM and fast vCPUs handle 20+ concurrent agent sessions. GPU unused by OpenClaw itself.

32GB and Zen 4. Handles multiple agents and services concurrently. Best value mini PC for a dedicated AI host.

12th-gen i5 with 16GB is more than enough. Good always-on server.

Runs fine as a background service. No local models but all cloud features work well.

The sweet spot for SBC hosting. Multi-channel messaging, browser automation, and community skills all work. 8GB gives breathing room.

Standard cloud deployment. No local models but all cloud API integrations work.

Works in desktop mode on SteamOS. 16GB RAM is plenty. Quirky but functional.

RK3588S handles the Node.js runtime well. 8GB RAM is comfortable. Good alternative to Raspberry Pi 5.

Termux + proot-distro provides full Linux. 8GB RAM is workable. Uses cloud APIs only.

8GB RAM is fine but JH7110 RISC-V is slower than ARM equivalents. Node.js works but not optimized.

8GB RAM and A311D2 handle OpenClaw well. Good I/O for concurrent API calls.

x86 compatibility is nice. N5105 and 8GB RAM run OpenClaw without ARM quirks.

8GB is enough. RISC-V Node.js is slower than ARM but works. Expect 2x latency vs Pi 5.

Same JH7110 as Star64. 8GB RAM handles OpenClaw. NVMe support helps with I/O.

M1 is still fast. 8GB unified memory is tight for local models but cloud APIs work great.

Graviton3 ARM is fast but 4GB is tight. Functional for light usage.

Termux + proot-distro. 8GB handles it but battery drain is significant.

16GB in Termux with proot-distro. Enough RAM for vanilla OpenClaw. Battery drain is the main concern.

8GB in Termux with proot. DeX mode makes it feel like a desktop. Functional but quirky.

Same APU as LCD model but better thermals and faster NVMe. Desktop mode works.

N5095 and 8GB in Docker alongside NAS duties. Works but competes for resources with QTS.

i5-7500 and 8GB. Old but functional. Cloud APIs only. Best $120 dedicated box.

4GB meets minimum but RK3399 is old and slow eMMC hurts. Barely usable.

4GB meets minimum but TDA4VM is quirky. Community support for AI workloads is limited.

4GB meets minimum but RK3328 is sluggish. Expect slow response times.

Meets minimum but tight. Gateway starts but expect GC crashes under load.

6GB minus Android overhead leaves maybe 3GB. OpenClaw starts but crashes under load.

Only 2GB RAM. Docker available but tight. Better to upgrade to 6GB+ first.

4GB with DSM overhead leaves maybe 2.5GB for Docker. Tight but starts. Upgrade RAM first.

4GB with ADM overhead. N5105 is capable but RAM is the bottleneck.

4GB CM4 with Home Assistant already running. Very tight. Possible but risky.

4GB with Umbrel overhead. OpenClaw starts but competes with other apps for memory.

Same as Pi 4 (4GB). Tight but functional. Keyboard form factor is charming.

4GB and quad A53 meet minimum but barely. Edge TPU doesn't help OpenClaw.

RK3399 is showing its age. 4GB RAM meets minimum but expect GC pauses.

4GB meets minimum. A73 cores are decent but memory is the bottleneck.

Linux container (Crostini) adds overhead. 8GB shared with ChromeOS. Sluggish but works.