Quad RTX 5060 Ti build targets Qwen 3.6 27B at Q8 speed
PCIe 5.0 x4 adapters deliver 8x PCIe 4.0 bandwidth for 4 GPUs.
A Reddit enthusiast has assembled a quad-RTX 5060 Ti 16GB system for AI inference, leveraging discounted hardware and creative slot expansion. The build uses an MSI MEG Z890 Unify-X motherboard with two PCIe slots (x8 and x4) plus two additional GPUs connected via M.2-to-PCIe adapters — each operating at PCIe 5.0 x4, which is bandwidth-equivalent to PCIe 4.0 x8. Two power supplies handle the load, with one shared across the adapters using a Y-splitter.
The cards themselves support impressive memory overclocks: four of the five RTX 5060 Tis achieve +6000 MT/s (+3000 MHz), significantly improving memory bandwidth critical for LLM inference. The system runs Ubuntu 26.04 with NVIDIA’s open kernel modules featuring P2P support. While benchmarks are pending, the owner aims to run Qwen 3.6 27B at Q8 (8-bit integer) using llama.cpp or vLLM, potentially achieving strong token-generation speeds for a modest total investment.
- Four RTX 5060 Ti 16GB cards using PCIe 5.0 x4 slots via M.2 adapters (bandwidth equivalent to PCIe 4.0 x8).
- Memory overclock of +6000 MT/s on four of five cards, boosting bandwidth for LLM workloads.
- Target model: Qwen 3.6 27B at Q8 (INT8) using llama.cpp or vLLM — benchmarks pending.
Why It Matters
Affordable multi-GPU inference setups are now possible with RTX 5060 Ti and novel adapter solutions.