For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /get-started.md).

Mojo package

gpu

GPU multi-head attention (MHA), cross-attention, and multi-head latent attention (MLA) kernels. Vendor-specific implementations live under amd/ and nvidia/.

Packages

• ​amd_rdna: TileTensor-native attention kernels for AMD RDNA3+ (gfx11xx/gfx12xx).
• ​amd_structured: TileTensor-native attention kernels for AMD gfx950 (MI355X).
• ​apple: Apple (Metal) GPU attention kernels.
• ​nvidia: NVIDIA GPU attention kernels and tile-scheduling utilities.

Modules

• ​mha: GPU flash-attention kernels and dispatch logic for prefill and decode.
• ​mha_cross: Implements a naive GPU multihead cross attention kernel supporting ragged batched inputs and a paged KV cache.
• ​mha_decode_partition_heuristic: Split-K partition-count heuristics for MHA and MLA decode kernels.
• ​mla: GPU kernels for Multi-head Latent Attention (MLA) decoding and prefill.
• ​mla_decode_dispatch_scalars: Device-dispatched MLA decode dispatch-metadata scalars.
• ​mla_graph: Provides manually fused GPU graph kernels for Multi-head Latent Attention (MLA).
• ​mla_index_fp8: MLA FP8 index kernel for computing attention scores with paged KV cache.
• ​sparse_index_fp8_sm100: SM100 (B200) tensor-core FP8 MLA lightning-indexer score kernel.
• ​sparse_index_fp8_sm100_prefill: SM100 (B200) warp-specialized PREFILL variant of the FP8 MLA indexer scorer.
• ​sparse_indexer_common: Shared device functions for the MiniMax-M3 sparse-attention (MSA) indexer.