June 2026 monthly summary for tenstorrent work focusing on MLA support, sharding enhancements, and platform uplift across TT-MLIR and TT-XLA: Key features delivered: - End-to-end MLA support path additions in TT-MLIR/TTNN: new flash_mla_prefill op for prefill and custom sharding/lowering for paged_flash_mla_decode, including SHLO->TTIR lowering, TTIR->TTNN conversion, and runtime integration. Built out flatbuffer/TTNNToFlatbuffer schemas and added code emission for C++/Python. Added SHLO builder support and golden mappings for MLA ops; comprehensive tests. - Custom sharding rules for gather (1D tensors and semantics): introduced dedicated sharding rules for tenstorrent.gather/gather_dim, with 1D tensor support and a workaround path to TTIR/TTNN, including op renaming and test coverage. - vLLM MLA integration in TT-XLA: added a custom MLA backend and operations for prefill and decode, new MLA classes (TTMLAAttentionBackend, Impl, and TTMLAAttention), and vLLM model_runner changes for MLA KV cache to concatenate KV tensors. This enables MLA workloads in vLLM with the appropriate backend and operator mapping. - MLA optimization enablement and bug fixes: fixed issues running MLA models via vLLM with optimizer enabled, aligned OpModel with programConfig for paged Flash MLA decode, and added MoE workarounds when optimizer is enabled. - TT-Metal uplift and compatibility updates: upgraded third_party/tt-metal to a newer commit, adjusted Dockerfiles/headers, and updated unit tests to reflect new requirements. Major bugs fixed: - Resolved MLA model runtime issues in vLLM by enabling optimizer and applying MoE workarounds; ensured OpModel uses consistent programConfig for paged Flash MLA decode, improving stability and throughput for MLA workloads. Overall impact and accomplishments: - Enabled end-to-end MLA workloads in both the compiler/IR path (TT-MLIR/TTNN) and runtime backends (TT-XLA), unlocking performance and capability for next-gen models in vLLM and related deployments. - Improved sharding semantics for key ops (gather) and robust support for 1D tensors, reducing manual workarounds and accelerating future feature development. - Strengthened platform stability and compatibility through TT-Metal uplift, enabling smoother integration with updated toolchains and runtimes. Technologies/skills demonstrated: - Compiler stacks: SHLO, TTIR, TTNN dialects, StableHLO lowering, EmitC/EmitPy code emission, test harnesses, and builder/golden mapping. - Runtime and ML workloads: MLA prefill/decode paths, KV cache management, and MoE/optimizer interplay in vLLM. - Infrastructure: Dockerfiles, header compatibility, FlatBuffers schemas, and cross-repo integration testing.

May 2026 monthly summary focused on delivering deep-learning model enhancements, improving test coverage, and advancing CI readiness across two repositories. Key outcomes include a new DeepSeek v4 model with optimization-ready design and a comprehensive DSv4 test suite with end-to-end validation, enabling faster feedback and higher reliability for production deployments.

April 2026 monthly summary focusing on delivering enhanced operator composition, lowered complexity, and broader operator coverage for TT-xla and TT-MLIR. Key impact includes improved graph execution efficiency, expanded operator support, and stronger testing coverage across multi-device scenarios, driving measurable business value in performance and reliability for model deployment on TT hardware.

2026-03 Monthly Summary for tenstorrent/tt-mlir: Implemented robust TopK support in TTNN via SHLO composite ops across three variants, with input dtype constraints (bfloat16/bfloat8) and output dtype alignment to improve usability and performance. Updated the ReoutlineComposite pass to preserve original result ordering (reoutline.result_pos), fixing ordering issues that affected TopK semantics when lowering. Introduced a mesh_partition crash workaround for TTNN with TILED 1D tensors by forcing ROW_MAJOR inputs, including test typo correction and optimizer-path coverage. Expanded test coverage to validate TopK SHLO composites, ordering, and mesh_partition changes. These changes deliver reliable Torch TopK integration, deterministic results, and improved stability in optimizer-enabled ML pipelines. Technologies demonstrated include TTIR/TTNN SHLO composites, ReoutlineComposite passes, and comprehensive test strategies.

Month: 2026-02 — Consolidated delivery across two Tenstorrent repositories (tt-xla and tt-mlir) with a focus on performance, robustness, and maintainability. Delivered feature work with robust tests, implemented efficient lowering patterns, and established groundwork for scalable model optimization.

January 2026 (2026-01) Feature delivery in tenstorrent/tt-mlir focused on Efficient SHLO Output Tensor Handling. Implemented Identity typing for all output ttir.mesh_shard ops so each device retains only its own shard, eliminating cross-device duplication of output tensors and reducing unnecessary memory use and communication in SHLO graph outputs. Updated runtime tests to align with the new behavior and prepared groundwork for future runtime support for evaluating graphs with sharded outputs. This work strengthens Torch-XLA's awareness of output shardings and lays the foundation for scalable SHLO pipelines.

Month 2025-12: Key delivery focused on correctness and pipeline stability in tt-mlir. Addressed a critical correctness issue in the Pattern Rewriter when converting Sdy CCLs to SHLO CCLs by switching the traversal strategy from bottom-up to top-down. This change ensures shapes update in the correct order, preventing pass failures when the output of one Sdy CCL feeds into another. The fix reduces regression surface, improves reliability for downstream MLIR passes, and aligns with ticket https://github.com/tenstorrent/tt-mlir/issues/6157 and PR #6421.

November 2025 monthly summary for tenstorrent/tt-forge-models: Implemented Qwen 2.5 Bias Sharding Optimization to distribute parameters across devices, improving efficiency and scalability. The change is captured in commit 814347af324c748fbed797e2cb8199da4efafd61 with message 'Add bias sharding for Qwen 2.5 models (#273)'. This work increases throughput for large-scale inference and lays the groundwork for future multi-device training in tt-forge-models.

October 2025: Delivered targeted features for distributed inference and dialect integration, while stabilizing multi-chip TP workloads. Key outcomes include Shardy dialect support in Torch-XLA with an OpenXLA StableHLO pipeline, Tensor Parallel sharding specs for Mistral and Qwen 3 models, and a stabilization fix that reverted composite operations in tt-xla to restore nightlies. These workstreams collectively improve scalability, reliability, and readiness for production-scale inference, and demonstrate cross-repo collaboration and advanced XLA/TP techniques.

2025-08 Monthly Summary: Focused on delivering demonstrable tensor-parallel capabilities, expanding CI coverage for parallelism workflows, and stabilizing dependencies to reduce build/import issues. The month produced tangible demos, improved validation coverage, and a more reliable baseline for tensor-parallel development across three repositories.

The June 2025 monthly summary highlights the rollout of testing infrastructure and CI enhancements for data-parallel workloads in the tenstorrent/tt-torch repository, along with a critical to_host fix and the introduction of a new test-logging utility. These changes stabilize and accelerate feedback on distributed tensor operations, align CI with data-parallel scenarios, and demonstrate strong technical execution with tangible business value in reliability and developer productivity.

May 2025 achievements for tenstorrent/tt-torch: Delivered data-parallel execution in ModelTester across multiple devices; enhanced user onboarding with documentation for CompilerConfig and torch.compile; fixed ResNet demo to use devices in BackendOptions and integrated the ResNet demo into CI for automated testing. These changes improve multi-device scalability, reliability, and developer productivity, enabling faster validation and clearer configuration.

April 2025 - Tenstorrent/tt-torch monthly summary: Delivered multi-device support with a DeviceManager enabling acquisition and management of multiple devices for parallel processing, plus an API update to target a specific device during model compilation. Fixed a data-parallel multi-device compilation bug by isolating per-device options, ensuring distinct configurations per device. These changes improve scalability, reliability, and developer ergonomics, enabling customers to better utilize heterogeneous device pools with predictable compilation behavior.

March 2025 monthly summary for tenstorrent/tt-torch highlighting API modernization and expanded test coverage. Delivered two key features with targeted commits, reinforcing stability, compatibility, and risk reduction. Focused on business value by ensuring future-proof bindings and early issue detection across models.