Further Reading

Curated references that informed Atlas's design. Not exhaustive — just the papers, articles, and prior-art projects worth reading if you want to understand why Atlas is shaped the way it is.

Kernel engineering

  • FlashAttention-2 — Tri Dao (ICLR 2024). arXiv:2307.08691. Foundation of Atlas's prefill kernels — tiled online softmax, Q/K/V tiling with shared memory, causal masking.
  • FlashAttention-4 — Shah, Bikshandi, Zhang, Thakkar, Ramani, Dao (2025). arXiv:2603.05451. Conditional softmax rescaling (skip ~90% of rescale ops), software polynomial exponential (sw_exp, avoids SFU bottleneck).
  • FlashInfer — Ye et al. (MLSys 2025 Best Paper). arXiv:2501.01005. Block-sparse paged KV, gather-SMEM-MMA pattern. Informed Atlas's paged decode kernel.
  • SageAttention 3 — Zhang et al. (NeurIPS 2025). arXiv:2505.11594. Native FP4 attention on newer Blackwell. Planned direction when SM12x+ silicon lands.
  • LeanAttention — Roy, Vassilieva, Willke, Mendis (2024). arXiv:2405.10480. Stream-K tile scheduling for decode attention. Planned for SM occupancy improvements.
  • CUTLASS documentation and examples. The BF16 MMA fragment shapes and cp.async pipelining patterns in Atlas's kernels follow CUTLASS conventions.

State-space models

  • Mamba: Linear-Time Sequence Modeling with Selective State Spaces — Gu, Dao (2023). arXiv:2312.00752. The original selective SSM.
  • Mamba-2 — Dao, Gu (ICML 2024). arXiv:2405.21060. The variant Nemotron-H uses.
  • Gated Delta Networks — Yang, Dao, et al. (2024). Closer to Qwen3.5's GDN formulation.
  • GDN register-tile results — Atlas's internal experiments in gdn_regtile_results.md track tile-shape tradeoffs on GB10.

Quantization

  • NVFP4 / FP4 microscaling — NVIDIA's blog posts on Blackwell FP4. The public docs for SM120 coverage are thin; much of Atlas's SM121 workaround has no upstream equivalent yet.
  • SmoothQuant — Xiao, Lin, et al. (ICML 2023). Scale factor calibration ideas used indirectly in Atlas's FP8 KV calibration.
  • Compressed-Tensors format — the HF compressed-tensors library's on-disk FP8 block-scaled layout.
  • TurboQuant (Atlas internal) — docs/turboquant-plus.md. WHT + Lloyd-Max 4-bit KV cache with ~2× lower MSE than NVFP4 at the same bit rate.

Speculative decoding

  • Fast Inference from Transformers via Speculative Decoding — Leviathan, Kalman, Matias (ICML 2023). The foundational paper.
  • Medusa: Multi-Token Prediction Heads — Cai et al. (2024). MTP-style draft heads.
  • Self-speculative Decoding — Layer-skipping drafter. Atlas implements a variant.

Constrained decoding

  • XGrammar — Li, Chen, Chen, et al. (2024). arXiv:2411.15100. The token-bitmap automaton approach Atlas uses.
  • SGLang — Zheng et al. (NeurIPS 2024). Broader exploration of structured-output techniques; Atlas shares design elements with SGLang's regex_fsm.

Inference systems

  • vLLM — Kwon et al. (SOSP 2023). The PagedAttention paper. Atlas's paged KV cache follows the vLLM model with Atlas-specific kernel work below it.
  • TensorRT-LLM documentation and source. Atlas's TRT-LLM benchmark comparisons in docs/ATLAS_SPARK_JOURNEY.md are informed by reading the TRT-LLM codebase; that journey records the 29.6 tok/s ceiling for NVFP4 on SM121 TRT-LLM.
  • SGLang — structured-generation inference framework.
  • Triton — inference server. Peripheral; Atlas does not use it but the operational patterns are informative.

MoE routing

  • Mixture-of-Experts with Sigmoid Routing — various 2023–2024 papers. MiniMax-M2.7's 256-expert sigmoid-routed MoE is an unusual design Atlas had to support natively.
  • Switch Transformer — Fedus, Zoph, Shazeer (2021). The top-1 routing baseline.

Adjacent projects worth studying

  • scitix/InstantTensor — the fast safetensors loader Atlas's O_DIRECT + pipelined reader is modeled on.
  • huggingface/tokenizers — the tokenizer library Atlas wraps.
  • huggingface/safetensors — format spec + Rust impl.
  • PyKeOps / symbolic autograd — not used, but the philosophy (compile once per shape, amortise forever) resonates with AI Kernel HyperCompiling.

Atlas-internal references

Inside the repo, the canonical long-form references are the architecture decision records in docs/adr/, plus the top-level notes alongside them. Notable:

  • docs/adr/0004-nvfp4-fp8-quantization.md — NVFP4/FP8 quantization, including why --kv-high-precision-layers exists.
  • docs/adr/0003-hybrid-ssm-attention.md — hybrid SSM/attention design and chunked SSM prefill.
  • docs/adr/0007-tp-ep-composition.md, docs/adr/0011-ep-batched-decode-optimization.md — EP=2 MoE dispatch and batched decode.
  • docs/adr/0010-vendor-xgrammar.md — constrained decoding via vendored XGrammar.
  • docs/turboquant-plus.md — TurboQuant KV.
  • docs/ARCHITECTURE.md, docs/ATLAS_KERNELS.md, docs/HARDWARE.md — the system, kernel, and hardware overviews.

For the broader research context that informed Atlas's direction, see docs/atlas-spark-research-articles.md in the repo — a rolling curated list that's longer and more current than this page.