diff --git a/README.md b/README.md
index f8e301ed..cbd3633e 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,8 @@
 
 Flux is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable.
 
+You need to build Julia 0.6 from source and have CUDA available to use Flux with GPUs – please see the [CUDAnative.jl](https://github.com/JuliaGPU/CUDAnative.jl) instructions for more details.
+
 ```julia
 julia> Pkg.add("Flux")
 ```
diff --git a/docs/src/gpu.md b/docs/src/gpu.md
index 253904ad..8fd36f98 100644
--- a/docs/src/gpu.md
+++ b/docs/src/gpu.md
@@ -1,5 +1,7 @@
 # GPU Support
 
+You need to build Julia 0.6 from source and have CUDA available to use Flux with GPUs – please see the [CUDAnative.jl](https://github.com/JuliaGPU/CUDAnative.jl) instructions for more details.
+
 Support for array operations on other hardware backends, like GPUs, is provided by external packages like [CuArrays](https://github.com/JuliaGPU/CuArrays.jl). Flux is agnostic to array types, so we simply need to move model weights and data to the GPU and Flux will handle it.
 
 For example, we can use `CuArrays` (with the `cu` converter) to run our [basic example](models/basics.md) on an NVIDIA GPU.
diff --git a/docs/src/index.md b/docs/src/index.md
index 86c9c3dc..afeb2075 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -14,3 +14,5 @@ Pkg.test("Flux") # Check things installed correctly
 ```
 
 Start with the [basics](models/basics.md). The [model zoo](https://github.com/FluxML/model-zoo/) is also a good starting point for many common kinds of models.
+
+See [GPU support](gpu.md) for more details on installing and using Flux with GPUs.