SYSTEM Cited by 1 source
Mako (Netflix ML compute platform)¶
Mako is Netflix's internal ML compute platform. It provisions GPUs on AWS and is the substrate that sits underneath open-source components like PyTorch, Ray, and vLLM in Netflix's Post-Training Framework stack. First canonical wiki reference: sources/2026-02-13-netflix-scaling-llm-post-training-at-netflix.
Definition¶
"At the base is Mako, Netflix's internal ML compute platform, which provisions GPUs on AWS." (Source: sources/2026-02-13-netflix-scaling-llm-post-training-at-netflix)
Role¶
Mako is the GPU-provisioning compute substrate — Netflix's ML equivalent of a scheduler/allocator layer — above which the rest of the LLM post-training stack runs:
Post-Training Framework ← library above
PyTorch / Ray / vLLM ← OSS, unmodified
Mako ← this page: GPU provisioning on AWS
AWS GPU instances
This is a foundational-platform-plus-domain-libraries pattern: Mako handles compute allocation generically, domain-specific libraries (post-training framework, presumably others for inference/pretraining) live above it.
What the source reveals¶
- Provisions GPUs on AWS (not on-prem).
- Intended to be the stable lower layer: the post-training framework runs PyTorch/Ray/vLLM "largely out of the box" on top of Mako, and the framework's four-pillar value-add (Data/Model/Compute/Workflow) sits above that.
What the source does not reveal¶
- Relationship (if any) to systems/metaflow, Netflix's other ML platform.
- Whether Mako handles scheduling, quota, capacity management, or just provisioning.
- Cluster topology, networking fabric, or job-lifecycle primitives.
- Whether Mako is Netflix's "single ML platform" or one of several.