diff --git a/docs/CUDA-WORKER.md b/docs/CUDA-WORKER.md
index 2891f1c..9c197ab 100644
--- a/docs/CUDA-WORKER.md
+++ b/docs/CUDA-WORKER.md
@@ -1,63 +1,71 @@
 # CUDA Worker Setup — T490s + 2080 Ti eGPU on the tailnet
 
-Goal: a headless Linux box with the RTX 2080 Ti (11GB) eGPU, live on Tailscale, that
+Goal: a headless box with the RTX 2080 Ti (11GB) eGPU, live on Tailscale, that
 MODELBEAST (on the M3 Ultra) offloads the CUDA-only jobs the Mac can't run —
 gaussian-splat → mesh extraction (SuGaR/2DGS), research-grade video mocap
 (GVHMR/WHAM/TRAM), NeRF/tiny-cuda-nn, nvdiffrast/PyTorch3D.
 
-**Why a ThinkPad and not the Intel iMac:** no T2 chip = no Secure-Boot/Thunderbolt
+**Why the T490s and not the Intel iMac:** no T2 chip = no Secure-Boot/Thunderbolt
 fight; laptop idles ~6–10W (+ ~20W eGPU) vs ~55–75W for the iMac; battery = built-in
-UPS. The 2080 Ti is EOL-ish (Turing: no bf16/fp8, 11GB) — it's a *compatibility*
-box, not a performance one. It will not beat the M3 Ultra at anything the Mac
-already does; it only unlocks the CUDA-locked tail.
+UPS. The 2080 Ti is EOL-ish (Turing: no bf16/fp8, 11GB) — a *compatibility* box, not
+a performance one. It won't beat the M3 Ultra at anything the Mac already does; it
+only unlocks the CUDA-locked tail.
 
-## Do this at the shop (once)
+**A physical GPU can't be passed to a VM on a Mac/Windows consumer hypervisor** — so
+the CUDA OS must have direct hardware access. On this ThinkPad that's either native
+Windows (already installed) or WSL2 (a real Linux env Windows gives GPU access to).
+NOT a Linux VM on macOS.
 
-1. **Ubuntu 24.04 LTS**, bare metal (dual-boot Win11 or wipe — bare-metal, NOT a VM;
-   macOS/Windows hypervisors can't pass a physical GPU through, so a Linux VM can't
-   do CUDA on the eGPU).
+---
 
-2. **Thunderbolt authorize the eGPU** (plug in the Razer Core first):
-   ```bash
-   sudo apt install bolt
-   boltctl list                      # find the enclosure UUID
-   boltctl enroll --policy auto <UUID>
+## Path A — Keep Windows 11 + WSL2  (RECOMMENDED: Win11 already runs, no reinstall)
+
+Windows drives the eGPU; WSL2 gives a clean Ubuntu where the Linux-first 3D repos
+build properly (native-Windows builds of their custom CUDA kernels are a pain).
+
+1. **NVIDIA Windows driver** (Studio or Game Ready) with the 2080 Ti plugged in.
+   Confirm Device Manager → Display adapters shows the 2080 Ti. This one driver is
+   all WSL2 needs — do NOT install a driver inside WSL.
+2. **WSL2 + Ubuntu** (admin PowerShell): `wsl --install -d Ubuntu` → reboot.
+3. **CUDA toolkit INSIDE WSL** (the `wsl-ubuntu` package — toolkit only, no driver),
+   per NVIDIA's "CUDA on WSL" guide. Then verify: `nvidia-smi` inside Ubuntu lists
+   the 2080 Ti. (If empty: update the Windows driver; ensure eGPU present at boot.)
+4. **Reach WSL from the tailnet cleanly** — create `C:\Users\<you>\.wslconfig`:
    ```
-
-3. **NVIDIA driver + CUDA:**
-   ```bash
-   sudo ubuntu-drivers autoinstall   # or the latest official driver branch
-   sudo apt install nvidia-cuda-toolkit
-   sudo reboot
-   nvidia-smi                        # must list the 2080 Ti over Thunderbolt
+   [wsl2]
+   networkingMode=mirrored
    ```
-   If it doesn't appear: confirm the eGPU was connected at boot, re-check `boltctl`,
-   ensure the `thunderbolt` module loads before `nvidia_drm`.
+   (Win11 22H2+; makes WSL share the host's network so Tailscale/SSH reach it.)
+5. **Tailscale** on the Windows host: install, sign in, `tailscale ip -4` → note it.
+6. **Headless Windows settings:** Power → "when I close the lid: do nothing" + never
+   sleep; enable **OpenSSH Server** (Settings → Optional Features); set Windows Update
+   active hours wide / pause auto-reboots so it never reboots mid-job.
+7. Access model: MODELBEAST SSHes to the Windows host over Tailscale and runs CUDA
+   tools via `wsl bash -lc "..."`, or SSH straight into WSL with mirrored networking.
 
-4. **Tailscale:**
-   ```bash
-   curl -fsSL https://tailscale.com/install.sh | sh
-   sudo tailscale up --ssh          # --ssh lets the M3 Ultra SSH in over the tailnet
-   tailscale ip -4                   # note this IP → put it in MODELBEAST settings
-   ```
+## Path B — Bare-metal Ubuntu  (leaner/lower-power, if you'd rather wipe Win11)
 
-5. **Headless / low-power:** run lid-closed →
-   `sudo sed -i 's/#HandleLidSwitch=suspend/HandleLidSwitch=ignore/' /etc/systemd/logind.conf`
-   then `sudo systemctl restart systemd-logind`. Enable Wake-on-LAN in BIOS + `ethtool`
-   if you want it to sleep and wake on demand.
+1. Ubuntu 24.04 LTS bare metal.
+2. `sudo ubuntu-drivers autoinstall && sudo apt install nvidia-cuda-toolkit && sudo reboot`; `nvidia-smi` must show the 2080 Ti.
+3. `curl -fsSL https://tailscale.com/install.sh | sh && sudo tailscale up --ssh`.
+4. Lid-closed headless: set `HandleLidSwitch=ignore` in `/etc/systemd/logind.conf`.
+   (No Thunderbolt-authorization dance needed on a non-Mac — it enumerates on boot.)
 
-## MODELBEAST integration (I'll build this when the box exists)
+---
+
+## MODELBEAST integration (I'll build this once the box is on the tailnet)
 
 The operator framework already shells out to tools; a **remote-CUDA operator** wraps
-that pattern: rsync the input to `cuda_host:~/work/`, `ssh cuda_host` to run the CUDA
-tool in its venv, rsync results back, register as assets. Planned operators:
-- `sugar_mesh` — splat `.ply` → textured mesh (closes the Brush→mesh gap; the Mac can't extract meshes from splats).
-- `gvhmr_mocap` — video → world-grounded SMPL → FBX (research-grade, replaces the cloud/manual path).
+that: rsync the input to the worker, `ssh` (→ `wsl` on Path A) to run the CUDA tool
+in its env, rsync results back, register as assets. Add a `cuda_host` setting (tailnet
+IP + user) to the vault. Planned:
+- `sugar_mesh` — splat `.ply` → textured mesh (closes the Brush→mesh gap; the Mac can't extract meshes from splats). ~5–6GB, fits 11GB.
+- `gvhmr_mocap` — video → world-grounded SMPL → FBX (research-grade; replaces the cloud/manual path). Light.
 
-Add a `cuda_host` setting (the tailnet IP + user) to the vault so these operators know where to reach it. Fits in 11GB: SuGaR/2DGS/gsplat (~5-6GB), GVHMR/WHAM (light). Does NOT fit: FLUX, 20B+ models — those stay on the Mac.
+Does NOT fit 11GB: FLUX, 20B+ models — those stay on the Mac.
 
 ## Reality check
-Only worth it because the card's already owned. If you were buying, a used RTX 3090
-(24GB, bf16) would be strictly better and unlock FLUX/video too. Turing's runway is
-shrinking (newer 3D wheels drop 20-series), so treat this as "use the free card for
-the CUDA-only leftovers," not a long-term foundation.
+Worth it only because the card's already owned. If buying, a used RTX 3090 (24GB,
+bf16) is strictly better and unlocks FLUX/video too. Turing's runway is shrinking
+(newer 3D wheels drop 20-series). Use the free card for the CUDA-only leftovers, not
+as a foundation.