diff --git a/.github/workflows/kcpp-build-release-linux-cuda12.yaml b/.github/workflows/kcpp-build-release-linux-cuda12.yaml
index 2d7eb3b41..72ef79c94 100644
--- a/.github/workflows/kcpp-build-release-linux-cuda12.yaml
+++ b/.github/workflows/kcpp-build-release-linux-cuda12.yaml
@@ -10,7 +10,7 @@ on:
 
 env:
   BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-  KCPP_CUDA: 12.4.0
+  KCPP_CUDA: 12.1.0
   ARCHES_CU12: 1
 
 jobs:
diff --git a/.github/workflows/kcpp-build-release-linux.yaml b/.github/workflows/kcpp-build-release-linux.yaml
index 13407f641..7614d5050 100644
--- a/.github/workflows/kcpp-build-release-linux.yaml
+++ b/.github/workflows/kcpp-build-release-linux.yaml
@@ -52,9 +52,6 @@ jobs:
         run: |
           ./koboldcpp.sh dist
 
-      - name: Rename file before upload
-        run: mv dist/koboldcpp-linux-x64-cuda1150 dist/koboldcpp-linux-x64-cuda11
-
       - name: Save artifact
         uses: actions/upload-artifact@v4
         with:
diff --git a/README.md b/README.md
index acda13677..a2d36d289 100644
--- a/README.md
+++ b/README.md
@@ -33,11 +33,11 @@ KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models
 - You can also run it using the command line. For info, please check `koboldcpp.exe --help`
 
 ## Linux Usage (Precompiled Binary, Recommended)
-On modern Linux systems, you should download the `koboldcpp-linux-x64-cuda11` prebuilt PyInstaller binary for greatest compatibility on the **[releases page](https://github.com/LostRuins/koboldcpp/releases/latest)**. Simply download and run the binary (You may have to `chmod +x` it first). If you have a newer device, you can also try the `koboldcpp-linux-x64-cuda12` instead for better speeds.
+On modern Linux systems, you should download the `koboldcpp-linux-x64-cuda1150` prebuilt PyInstaller binary for greatest compatibility on the **[releases page](https://github.com/LostRuins/koboldcpp/releases/latest)**. Simply download and run the binary (You may have to `chmod +x` it first). If you have a newer device, you can also try the `koboldcpp-linux-x64-cuda1210` instead for better speeds.
 
 Alternatively, you can also install koboldcpp to the current directory by running the following terminal command:
 ```
-curl -fLo koboldcpp https://github.com/LostRuins/koboldcpp/releases/latest/download/koboldcpp-linux-x64-cuda11 && chmod +x koboldcpp
+curl -fLo koboldcpp https://github.com/LostRuins/koboldcpp/releases/latest/download/koboldcpp-linux-x64-cuda1150 && chmod +x koboldcpp
 ```
 After running this command you can launch Koboldcpp from the current directory using `./koboldcpp` in the terminal (for CLI usage, run with `--help`).
 Finally, obtain and load a GGUF model. See [here](#Obtaining-a-GGUF-model)
diff --git a/environment.yaml b/environment.yaml
index e65152853..d7aa7adcd 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -1,6 +1,6 @@
 name: koboldcpp
 channels:
-  - nvidia/label/cuda-12.4.0
+  - nvidia/label/cuda-12.1.0
   - conda-forge
   - defaults
 dependencies:
diff --git a/koboldcpp.sh b/koboldcpp.sh
index de5616508..8b6144c20 100755
--- a/koboldcpp.sh
+++ b/koboldcpp.sh
@@ -7,9 +7,9 @@ fi
 if [[ ! -f "conda/envs/linux/bin/python" && $KCPP_CUDA != "rocm" || $1 == "rebuild" && $KCPP_CUDA != "rocm" ]]; then
 	cp environment.yaml environment.tmp.yaml
 	if [ -n "$KCPP_CUDA" ]; then
-		sed -i -e "s/nvidia\/label\/cuda-12.4.0/nvidia\/label\/cuda-$KCPP_CUDA/g" environment.tmp.yaml
+		sed -i -e "s/nvidia\/label\/cuda-12.1.0/nvidia\/label\/cuda-$KCPP_CUDA/g" environment.tmp.yaml
 	else
-		KCPP_CUDA=12.4.0
+		KCPP_CUDA=12.1.0
 	fi
 	bin/micromamba create --no-rc --no-shortcuts -r conda -p conda/envs/linux -f environment.tmp.yaml -y
 	bin/micromamba create --no-rc --no-shortcuts -r conda -p conda/envs/linux -f environment.tmp.yaml -y