tensor-fusion

There's a third option that might fit some of the "I'm on a Mac but need CUDA" cases: network-mounting an Nvidia GPU from another machine on the same LAN. The GPU stays wherever it lives (office server, lab machine, a roommate's PC), your Mac runs the CUDA workload locally without any code changes — same PyTorch/CUDA calls, just intercepted by a stub library that forwards them over the local network.

The tradeoff vs. a physical eGPU: no Thunderbolt bandwidth ceiling or cabling, but you do need to be on the same LAN and there's ~4% overhead vs. native. Doesn't help if you need the GPU while traveling, and won't fix the physical macOS driver situation for native GPU access.

Disclosure: I work on GPU Go (tensor-fusion.ai/products/gpu-go), so I'm obviously biased toward this approach — but it genuinely is a different point in the design space from eGPU.

As more people carry ARM laptops and keep the GPU somewhere else, I think the interesting UX question becomes whether the GPU can "follow" the local workflow instead of forcing the whole workflow to move to the GPU host. That's the problem we've been looking at with GPUGo / TensorFusion: local-first dev flow, remote GPU access when needed. Curious whether people here mostly want true attached-eGPU semantics, or just the lowest-friction way to access remote compute from a Mac without turning everything into a remote desktop / VM workflow.

Interesting direction. One adjacent workflow we've been looking at is cross-environment execution where the agent / dev loop stays local, but GPU access lives elsewhere. In our case the recurring pain isn't only orchestration, it's making an existing remote GPU easy to attach to from a laptop or lab machine without shifting the whole workflow into a remote VM mindset. I'm involved with GPUGo / TensorFusion, so biased, but I think local-first + remote capability is going to matter a lot for small teams and labs. Curious whether you expect most users to want symmetric peer-style composition, or whether local-first control over remote resources ends up being the dominant pattern.

Interesting direction. One adjacent pattern we've been working on is a bit less about partitioning a shared node for more tokens, and more about letting developers keep a local workflow while attaching to an existing remote GPU via a share link / CLI / VS Code path. In labs and small teams we've found the pain is often not just allocation, but getting access into the everyday workflow without moving code + environment into a full remote VM flow. Curious whether your users mostly want higher GPU utilization, or whether they also want workflow portability from laptops and homelabs. I'm involved with GPUGo / TensorFusion, so that's the lens I'm looking through.