Runtime and Backends

Current DeepSeek RL launch and qualification entry: Miles baseline.

Supported roles

Path	Role	Qualification boundary
Speedrun	Cold small-model screens and historical regression oracle	Frozen arguments, packing, optimizer and data order
Megatron Core	Distributed pretraining and scaling	Model, optimizer grouping, topology, checkpoint and numerical tests
NeMo AutoModel	Pretrained Qwen/DeepSeek integration and post-training	Pinned upstream source, complete weight loading, FSDP/EP behavior
SGLang integration	Serving/export experiments	Checkpoint coverage, model plugin, cache behavior and inference parity

The CLI backend registry covers speedrun and Megatron. AutoModel and serving use dedicated entries.

Runtime contract

Use the validated container's interpreter and installed PyTorch, CUDA, NCCL, Transformer Engine, and Megatron packages. Record the container identity, imported paths, resolved versions, and upstream revision.

A support table or successful import is insufficient evidence for a new model/topology.

Parallelism

DP replicates data lanes; TP splits tensors; PP splits layers; CP splits context; EP partitions experts. FSDP shards model state across its owner group.

Record actual DP, TP, PP, CP, EP, expert-FSDP and table-owner groups. Expert count is an architecture choice; EP size is an execution choice.

More model parallelism can save memory while reducing speed. Choose topology from measured capacity and throughput.

Historical backend evidence

The matched 10M study found architecture-delta correlation 0.971361 and matching directions for 13/15 variants. The backends were not numerically interchangeable. Speedrun retained a cold-start advantage; Megatron improved steady-state throughput in its qualified configuration.

See Results and the versioned docs/BACKEND_COMPARISON.md report.