LM Studio users seek dual-GPU benchmarks

There is no single authoritative chart for this because multi-GPU inference speed depends on the engine, quantization, context size, PCIe lanes, and whether the cards have a fast interconnect. The practical answer is usually to benchmark your exact stack, not trust a generic “2 GPUs is faster” rule.

• –Consumer dual-GPU setups often hit PCIe bottlenecks, so the second card can add capacity without adding much speed
• –Backend choice matters a lot: llama.cpp, vLLM, and other runtimes can produce very different tok/sec on the same hardware
• –The post is really about a workflow tradeoff, not raw horsepower: interactive coding needs enough throughput to stay usable, not just a larger model window
• –LM Studio is relevant because it exposes local offload and MCP-friendly workflows, but the hardware economics still dominate the decision
• –The best public references are scattered benchmarks and per-project repos, so this is still a “measure your own stack” problem for serious buyers