diff --git a/environment-setup/configuration.mdx b/environment-setup/configuration.mdx index 5c465f5..ae87130 100644 --- a/environment-setup/configuration.mdx +++ b/environment-setup/configuration.mdx @@ -85,7 +85,7 @@ Auto-detected. ROCm is installed automatically on Ubuntu and RHEL/CentOS. A logo ### macOS -CPU only. Docker Desktop on macOS does not support GPU passthrough. For GPU workloads, deploy on a Linux machine with NVIDIA GPUs or use [AWS (EKS)](/environment-setup/eks-client-deployment-guide). +CPU only, on both Intel and Apple silicon. Training runs inside Linux containers, which on macOS run in a virtual machine with no access to the Apple GPU — so macOS machines train on CPU by design, regardless of device settings. For GPU training, use a machine with an NVIDIA GPU (Linux, or Windows via WSL2), or deploy on [AWS (EKS)](/environment-setup/eks-client-deployment-guide). ### Windows diff --git a/environment-setup/deployment-environments.mdx b/environment-setup/deployment-environments.mdx index 4298716..d5f886c 100644 --- a/environment-setup/deployment-environments.mdx +++ b/environment-setup/deployment-environments.mdx @@ -9,7 +9,7 @@ tracebloc runs the same way everywhere: one chart (`tracebloc/client`), one set | Environment | Runs on | GPU | Best when | |---|---|---|---| -| [Local / k3d](/environment-setup/deploy-local) | One machine you own | NVIDIA / AMD, auto-detected | A laptop or a single on-prem server — the fastest start | +| [Local / k3d](/environment-setup/deploy-local) | One machine you own | NVIDIA / AMD, auto-detected (macOS: CPU only) | A laptop or a single on-prem server — the fastest start | | [Bare-metal](/environment-setup/deploy-bare-metal) | Your own Kubernetes cluster | Your nodes | You already run on-prem Kubernetes | | [Amazon EKS](/environment-setup/eks-client-deployment-guide) | AWS (managed) | GPU nodegroups | You're on AWS and want managed, autoscaling compute | | [Azure AKS](/environment-setup/deploy-aks) | Azure (managed) | GPU node pools | You're on Azure |