The trouble started at the end of March, when someone on LocalLLaMA asked a deceptively simple question: how do you physically fit two RTX 3090s — cards notorious for their bulk — inside a single case without something catching fire or throttling the other? It wasn't a theoretical question. It was the kind of post that comes from someone staring at a parts pile on a desk. Two weeks later, a second thread surfaced with a sharper technical edge: a user running a PCIe bifurcation setup with two RTX 3060s and a 5060 Ti found that only one of the two 3060s appeared in nvidia-smi. The hardware was seated. The system booted. But the GPU simply wasn't there, as far as the software was concerned. Two separate people, two separate problems, both trying to run multiple GPUs for local inference — and both hitting walls that the official documentation doesn't quite prepare you for.
If confirmed as a pattern rather than isolated incidents, here is what this means: the enthusiasm for running local large language models has outpaced the practical knowledge base for multi-GPU consumer setups. Most tutorials assume a single GPU. PCIe bifurcation — splitting a single slot's bandwidth across multiple devices — requires motherboard support that varies wildly even within the same product line, and nvidia-smi invisibility is a known symptom of bifurcation misconfiguration or driver conflicts that many users simply won't diagnose correctly. The people being affected are the exact cohort that matters most to the local AI ecosystem: technically capable enthusiasts who are one frustrating weekend away from either solving the problem publicly and creating useful knowledge, or giving up and concluding that multi-GPU local inference isn't worth the effort. The second outcome would slow a genuinely useful democratisation of AI compute.
Watch for community-sourced solutions or confirmed motherboard compatibility lists emerging from these threads — or for similar reports accumulating across Reddit and Hugging Face forums, which would suggest this is friction at scale rather than two unlucky builders.
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t6d9la/9700_pro_users_undervolting_nets_crazy_clocks/"> <img alt="9700 pro users, undervolting nets crazy
<!-- SC_OFF --><div class="md"><p>Few days ago I posted about my <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t94ito/i_have_deepseek_v4_pro_at_home/">DeepSeek V4 Pro</a>
<h2> FlashQLA Kernels Accelerate AI; NVIDIA & AMD Unveil New GPUs </h2> <h3> Today's Highlights </h3> <p>This week, Qwen introduced FlashQLA, high-performance attention
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1sz0lyk/16x_dgx_sparks_what_should_i_run/"> <img alt="16x DGX Sparks - What should I run?"
<!-- SC_OFF --><div class="md"><p><strong>Geezuz, its fast!</strong></p> <p>Qwen3.8-27B, two RTX 3090s power-limited to 280 W, plain PCIe, no NVLink, at 0 Context:<br /> (tests
<!-- SC_OFF --><div class="md"><p>For weeks I've had problems with GPU constantly disconnecting and locking the OS. It would load the model fine, then crash when I passed the
<!-- SC_OFF --><div class="md"><p>My company just banned us from putting any proprietary data into clould services for security reasons. I need help deciding between 2 pc. My main
<!-- SC_OFF --><div class="md"><p>Hardware-----------------<br /> 4070 12gb - gen 4.0 x16<br /> v100 16gb - gen 3.0 x4</p> <p>32gb 3200mhz ram</p> <p>it5-12400f<br /> ssd read -
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