diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 89df162..307986d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -19,7 +19,7 @@ jobs:
         run: |
           # Create a hash from the environment file and PyTorch CPU install string
           ENV_HASH=$(sha256sum toolbox_env_conda.yml | cut -d' ' -f1)
-          PYTORCH_SPEC="pytorch-cpuonly"
+          PYTORCH_SPEC="pip-torch-torchvision-cpu-whl"
           CACHE_KEY="micromamba-${{ runner.os }}-${ENV_HASH}-${PYTORCH_SPEC}"
           echo "key=${CACHE_KEY}" >> $GITHUB_OUTPUT
 
@@ -54,8 +54,14 @@ jobs:
       - name: Install CPU-only PyTorch and dependencies
         if: steps.cache-restore.outputs.cache-hit != 'true'
         run: |
-          ~/micromamba-bin/micromamba install -n tbe -y pytorch cpuonly -c pytorch -c conda-forge
-          pip install esm
+          # Avoid a second micromamba/libmamba solve mixing pytorch + conda-forge on an
+          # already-large env (can abort with libsolv solver_addrule assertion, exit 134).
+          # CPU wheels from PyTorch are the usual CI approach. Install torchvision from the
+          # same index as torch so C++/dispatch ops (e.g. torchvision::nms) match conda-forge
+          # transformers alone pulls torchvision that can mismatch a pip-only torch build.
+          ~/micromamba-bin/micromamba run -n tbe python -m pip install --upgrade pip
+          ~/micromamba-bin/micromamba run -n tbe python -m pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
+          ~/micromamba-bin/micromamba run -n tbe python -m pip install esm
 
       - name: Save micromamba environment cache
         if: steps.cache-restore.outputs.cache-hit != 'true'