June 2026: Stability and reliability improvements in vllm-gaudi focusing on memory safety during warmup for non-GDN mamba-hybrid models. Delivered a targeted OOM prevention fix by capping context size and aligning decode bucketing with kernel granularity, reducing risk of crashes and improving tool-calling accuracy.

May 2026 monthly summary for vllm-gaudi: Focused delivery of features and fixes enabling reliable support for Qwen3NextForCausalLM with hybrid GDN+attention, and restoration of correct attention behavior across non-GDN hybrids. These efforts strengthen compatibility, stability, and production readiness, while showcasing strong technical execution and cross-team collaboration.

April 2026 monthly summary for vllm-gaudi: Delivered a new quantization path for HPU compressed tensors with BF16 fallback, enabling CompressedTensorsW8A8Int8 support and ensuring compatibility with existing operations. Implemented a new class HPUCompressedTensorsW8A8Int8_BF16Fallback, added unit tests, and registered an out-of-tree custom op to support the scheme. Integrated the scheme with get_hpu_scheme for seamless usage and prepared the code path to create, scale, and convert INT8 weights to BF16 before applying via standard linear ops.

February 2026 monthly summary for vllm-gaudi focus. Implemented architecture-aware configuration for use_qk_norm to differentiate between Scout and Maverick architectures, sourced directly from the global config rather than a local variable. This fixes incorrect behavior across architectures and improves model adaptability to different environments. Commit 3da5ef7d304aefb44fe71c298187824dcc77699c.

January 2026 monthly work summary focusing on stability improvements and multi-modal capabilities in the VLLM Gaudi projects. Delivered a robust crash-avoidance fix for HPUAttentionMetadataProcessor and enabled Llama4 Maverick multi-modal support across two repositories through targeted tensor shape handling and attention scaling adjustments. These efforts improve reliability for production workloads and broaden the applicability of Maverick-enabled inference pipelines.

December 2025: Delivered Llama4Attention HPU Compatibility Enhancement in vllm-gaudi, improving reliability and performance on HPU-backed models by monkey-patching _get_attn_scale. Linked to GAUDISW-243560 with commit f9dc033e68a1210727e4cdc4876ab827cae877d9. This work strengthens deploying Llama4Attention in GAUDI environments and reduces attention-scaling edge-case failures, enabling smoother inference on Intel hardware.