vLLM v0.24.0 Release Notes

Highlights

This release features 571 commits from 256 contributors (77 new)!

• MiniMax-M3: Added support for the new MiniMax-M3 model (#45381), with a fast follow-on of BF16/FP8 indexer via MSA (#45892), MXFP4 support (#45896), FP8 sparse GQA (#45744), and extensive AMD/ROCm tuning — mxfp8 MoE/linear on gfx950 (#45725), fp8_per_channel for bf16 weights on MI300X (#45854), FP8 KV-cache fix (#45720), and packed-modules mapping (#45794). A MiniMax-M2 perf regression was also fixed (#45935).
• DeepSeek-V4 keeps maturing: Following its debut, DeepSeek-V4 received another large optimization pass — a FlashInfer sparse index cache (2–4% TTFT) (#45863), prefill chunk-planning optimization (4% E2E throughput) (#45061), a cluster-cooperative topK kernel for low-latency (#43008), contiguous per-block KV allocations (#44577), TEP=16 for the block-FP8 shared expert (#46001), and native DSA indexer decode for next_n > 2 on SM100 (#45322). It is now enabled on SM120 alongside GLM-5.1 (#43477), with XPU (#44144, #44517, #45240) and ROCm (#44899, #45103, #45681) attention/MoE paths added.
• Model Runner V2 (MRv2) continues to expand: MRv2 now supports quantized models by default (#44446), enables GraniteMoE by default (#45461), and gained migration of Qwen + DeepSeek-V2 MoE models (#42667), DFlash speculative decoding (#44586), and more accurate FP32 Gumbel sampling (#45996).
• Streaming Parser Engine: A new streaming parser engine unifies tool-call/reasoning parsing across models, with parsers for Qwen3 (#45413), MiniMax-M2 (#45701), GLM-4.7/5.1/5.2 (#45915), and Nemotron V3 (#45755).
• Diffusion LLMs: Added DiffusionGemma (#45163), including a CPU path (#45690) and structured-output guardrails for diffusion decoders (#45468).
• WideEP / DeepEP v2: Integrated DeepEP v2 for expert parallelism (#41183), with follow-on robustness fixes (#46404, #46432).
• Rust frontend matures further: Added API-key authentication (#44321), CORS (#45753), /tokenize + /detokenize (#44222), /pause /resume /is_paused (#44499), /abort_requests (#44382), /get_world_size (#44801), thinking_token_budget (#46137), a Python bridge for Rust tool parsers (#44624), and many new parsers and validation paths.
• Device selection change: vLLM no longer sets CUDA_VISIBLE_DEVICES internally; a new device_ids argument is provided instead (#45026). On ROCm, a deprecation window for CUDA_VISIBLE_DEVICES has begun (#46636).

Model Support

• New models: MiniMax-M3 (#45381), DiffusionGemma (#45163) + Gemma Diffusion on CPU (#45690), Hierarchical Reasoning Model — Text / HrmTextForCausalLM (#43098), OpenMOSS (#44124).
• Gemma 4: Unified FlashAttention (FA4) across all layers + mm_prefix support (#42175); many parser/serving fixes — forced-JSON skip for required/named tool choice (#45795), parsing with thinking disabled (#45832), streaming reasoning-state init (#45852), reasoning rendering on assistant turns (#45867), offline-parser truncation/token-leak fix (#45553); legacy Gemma4 parsers replaced with an engine-based implementation (#45588).
• DeepSeek-V4: OOM fix (#44914), MTP projection prefixing (#44821), supported KV-cache dtypes (#44892).
• Qwen / multimodal: Qwen3-VL video loader (#44412), Qwen2-VL/Qwen2.5-VL processor-mapped video loader (#45555), Qwen3-VL multi-video processing optimization (#46026) and multi-video crash fix (#46305), Qwen3-Omni VIT cu_seqlens device fix (#44264), fused qk-rmsnorm-rope-gate for Qwen3.5 (#44176), Qwen3.5 EP weight-loading fix (#45002).
• ViT full CUDA graph: GLM-4.1V (#40576), DeepSeek-OCR dual-path (#43586), Kimi-VL (#41992), mllama4 (#40660), Lfm2VL encoder (#44930).
• Other model fixes: Llama4 weight loading (#45047) and streamed loading to avoid host-OOM (#44645), MiMo v2.x QKV TP sharding + FP4 (#45200), ColQwen3.5 retrieval correctness (#46108), EXAONE-4.5 vision encoder (#45073), MiDashengLM TP>1 audio-encoder crash (#44408), MiniCPM-o/V device-placement and image-size fixes (#43844, #42332, #44980, #45244), Cohere2 MoE weight loading + parser (#44747, #44907), Nemotron V3 reasoning-as-content (#39091), ColBERT AutoWeightsLoader + query/document embedding io processor (#44999, #45210).
• Kernels: GLM-5 TRT-LLM ragged MLA prefill dimensions (#43525), GLM-5 router GEMM (#46385).

Engine Core

• Model Runner V2: Quantized models by default (#44446), GraniteMoE default (#45461), Qwen/DSv2 MoE migration (#42667), DFlash (#44586), simplified async output handling (#45442), attention-group split on num_heads_q (#45564), LoRA warmup fix (#35536), more accurate FP32 Gumbel sampling (#45996), min_tokens off-by-one fix in the V2 GPU sampler (#46243), plus assorted model/config compatibility fixes (#45868).
• Speculative decoding: Dynamic SD (#32374); DFlash with FlashInfer (#43081), mixed KV page sizes (#45181), and Qwen3Next targets (#45319); EAGLE3 support for Qwen3 (#43132); reduced TP communication for large-vocab drafts (#39419); race fix in async accepted counts (#45100); EAGLE multimodal encoder cache fixes (#46315).
• KV cache & scheduler: KV-cache watermark to reduce preemptions (#44594), two-phase allocation for cross-group prefix-cache hits (#44409), Marconi-style admission policy for hybrid cache (#37898), prefix-cache retention for Mamba/linear attention (#45845), DS Mamba tail-copy for MTP align mode (#45473), reduced scheduler copy overhead (#45840).
• Attention: Re-enabled cross-layer KV cache layout for MLA via stride-aware kernels (#45111), MLA prefill FA4 fp8 output (#43050), FlexAttention custom mask mods made fully cudagraphable (#45232), triton diff-kv backend for MiMo (#41797), FlashMLA sparse accuracy fix (#36616).
• Weight loading & core: fastsafetensors ParallelLoader for weight loading (#40183), release of cached device memory under pressure on UMA GPUs (#45179), structured outputs for beam search (#35022), device_ids arg / no internal CUDA_VISIBLE_DEVICES (#45026), graceful fallback when numactl --membind is blocked (#45438), config-class registration before tokenizer init (#40299), async scheduling with prompt embeds for multimodal models (#45673).

Large Scale Serving & Distributed

• Expert parallel: DeepEP v2 integration (#41183) with token-bound and topk-index fixes (#46404, #46432); NIXL EP — DBO with NIXL EP (#45275), top-k index dtype query (#45298), NVFP4 post-receive quantization skip (#45606), elastic-EP communicator (#45013); reject NCCL-based EPLB with async EPLB (#44978).
• KV connectors / disaggregated serving: KV push from prefill to decode via NIXL (#35264); per-region KV transfer classification for mixed full-attn + MLA groups (#44583); Mooncake pipeline-parallel PD support (#44528), async lookup (#45659), compact chunk-hash zero-copy lookup (#45969), SWA-block skipping (#45444); P/D fixes with DP supervisor (#46628) and DSV4 disaggregation (#45831); removed P2pNcclConnector (#44854).
• KV offloading: Multi-tier async batched lookup (#44193), packed HMA KV-cache layout (#46205, gated #46252), parallel-agnostic fs-tier cache (#44733), offloading-manager stats (#35669) and labeled/CPU-usage metrics (#45957, #45737), self-describing KV events (#43468), non-blocking idle flush (#45595), and numerous correctness/race fixes (#44784, #45823, #46231, #46278).
• Distributed core: Prefill step cadence for better non-PD DP balancing (#44558), KV-event map encoding (#42892), one-shot fused all-reduce PDL NaN fix (#45448).

Hardware & Performance

• NVIDIA / kernels: SM90 CUTLASS FP8 mm odd-M support via swap_ab (180–290% kernel speedup) (#44572), tuned fused_moe FP8 for Qwen3-Next-80B on H100 (+25%) (#44830), native DSA indexer decode on SM100 (#45322), cluster-cooperative topK for DeepSeek low-latency (#43008), PDL support for DeepGEMM (#46006), FlashInfer cutedsl NVFP4 GEMM (#42235) and cute-dsl MXFP8 linear kernel (#46393), new Helion kernels for FP8/RMSNorm quant (#36902, #33790, #36895, #34432).
• torch stable ABI: Continued (and completed) migration of kernels to the libtorch stable ABI — MoE [10c/n] (#44565), Marlin [11a/n] (#45176), Machete [11b/n] (#45304), final _C library migration [12/n] (#45415).
• AMD ROCm: Torch 2.11 (#45362); fused AR + RMSNorm + per-group FP8 quant (#42864), fused softplus-sqrt-topk MoE router under AITER (#44945), DSv4 flash-decode split-K kernel (#44899) and inverse-RoPE fusion (#45103), W4A16 FlyDSL MoE (#44400), A8W4 MoE CDNA4 swizzle gate for gpt-oss (#44804); deprecation window begun for CUDA_VISIBLE_DEVICES on ROCm (#46636).
• Intel XPU: Sequence-parallel support (#38608), torch-xpu 2.12 (#42262), vllm-xpu-kernels v0.1.10 (#40367), W4A16 int4 group_size=32 MoE (#45136), DeepSeek-V4 attention/MoE paths (#44144, #44517, #45240), top-p sampling correctness fix (#44470).
• CPU & other architectures: 2.5× faster ASR CPU preprocessing via multi-threading (#44612), CPU W4A16 INT4 MoE (#43409), cgroup memory-limit-aware KV cache sizing (#45086), RISC-V oneDNN W8A8 INT8 (#44478) and RVV micro-GEMM for WNA16 (#44324), pinned memory for WSL2 (#41496), ZenCPU runtime logging (#42726).
• TPU: tpu-inference upgraded to v0.22.1 (#45793).
• Misc perf: VLLM_TRITON_FORCE_FIRST_CONFIG to skip Triton autotuning (#42425), Triton recompile detection (#45631), fused multi-group block-table staged writes (#44944).

Quantization

• Online & mixed-precision: Online FP8 per-token-per-channel (PTPC) quantization (#44132); modelopt_mixed support extended to Ampere/SM80-86 (#45306) and Turing/SM75 (#45375).
• FP4 / MXFP: FlashInfer cutedsl NVFP4 GEMM backend (#42235) and cute-dsl MXFP8 linear kernel (#46393), MXFP4 W4A4 MoE CUTLASS E8M0 scale fix (#43557), SwiGLU clamp wired for NVFP4 MoE on non-Blackwell (#45836), flashinfer_cutlass allowed as a clamped NVFP4 MoE backend (#46492), NVFP4/OCP MX MoE emulation fix (#46254), FP8 MoE re-enabled on NVIDIA Thor (#46339).
• GGUF / compressed-tensors / AWQ: GGUF quantization migrated to a plugin (#39612), compressed-tensors WNA16 MoE actorder fix (#41161) and KV-cache-scheme rejection (#45312), AWQ format on XPU (#43404) and AWQ dequantize fix on Intel XPU (#42727).
• Kernels & correctness: QuantizedActivation linear-kernel contract (#44260), consolidated Marlin thread-tile padding (#45295), FP8 weight layout canonicalized to (K, N) (#44735), corrupt-output fix for MoE FP8 with LoRAs loaded (#42120), symmetric-quant regression fix in GPTQ/CT MoE (#45656), fp8_e5m2 KV cache allowed for non-fp8 checkpoints (#45040).

API & Frontend

• Tool calling & parsing: Strict mode for tool calling in Chat Completions (#45003) and Responses API (#45396); new Streaming Parser Engine (#45413) with Qwen3, MiniMax-M2 (#45701), GLM-4.7/5.1/5.2 (#45915), Nemotron V3 (#45755) parsers; unified Parser consolidation in chat serving (#45548); numerous parser correctness fixes (#46047, #46091, #46159, #45763, #46351, #43984).
• OpenAI / Responses: Real /v1/embeddings support for messages + chat_template_kwargs (#45173), multimodal token counts in usage.prompt_tokens_details (#45458), omit empty tool_calls from chat responses (#44105), Responses API streaming function_call id fix (#44608), Harmony refactor of streaming/non-streaming paths (#45171, #45104).
• Anthropic Messages API: Cache-usage reporting in /v1/messages (#40912), mid-conversation system-message handling (#46025), inline system-message position preserved for prefix caching (#44602), tool_use argument-dropping fix (#45287).
• Rust frontend: API-key auth (#44321), CORS (#45753), /tokenize + /detokenize (#44222), /pause /resume /is_paused (#44499), /abort_requests (#44382), /get_world_size (#44801), thinking_token_budget (#46137), parallel_tool_calls=false (#44760), continuous usage stats (#43965), model metadata in /v1/models (#45950), Python bridge for Rust tool parsers (#44624), dedicated runtime for HTTP/ZMQ (#46051), and many validation/correctness fixes.
• Metrics: vllm:tool_call_parser_invocations_total (#44448), group-aware KV cache capacity in vllm:cache_config_info (#42206), MLA attention metrics for DeepSeek MFU estimation (#39457).
• Pooling / embeddings: Validation for Cohere /v2/embed input exclusivity (#45640), non-negative rerank top_n (#46119), matryoshka embedding dimension bounds (#46313).
• Benchmarks: BFCL tool-calling dataset for vllm bench serve (#42457), multi-turn benchmark api_key/custom headers (#44516), tokenizer-mismatch auto-correction (#44708).

Security

This release ships another coordinated security-hardening batch (much of it from security researcher @jperezdealgaba).

• Denial of service: Audio decompression bomb in the speech-to-text endpoint (#44970), remote DoS via invalid recovered-token reinjection in speculative decoding (#44744), DoS via prompt_embeds on M-RoPE models (#45252), regex-compilation timeout guard in structured outputs (#45118), audio upload size limit before full materialization (#45510), audio decode duration limit in the chat-completions path (#45908).
• Information disclosure: int32 truncation in the GGUF dequantize kernels (#44971).
• Input validation & hardening: Image EXIF orientation and tRNS transparency handling (#44974), rejection of non-finite temperature/repetition_penalty (#45116), sanitize_message applied to Anthropic and STT error paths (#45119).
• Dependencies: Upgrade Starlette to ≥ 1.0.1 to fix CVE-2026-48710 (#45675).

Dependencies

• Torch 2.11 on ROCm (#45362), torch-xpu 2.12 (#42262), tpu-inference v0.22.1 (#45793), NIXL v0.10.1 for XPU (#40287), Starlette ≥ 1.0.1 (#45675).
• mistral_common is now optional via deferred import (#45305); CUDA Dockerfiles upgraded from GCC 10 to GCC 12 for C++20 (#44923); spinloop extension skipped on Python < 3.11 (#44783).

Deprecations & Removals

• Removed models: ERNIE (obsolete) (#45127), Xverse (#45638), Dots1 (#45637), Bamba (#45990), Mono-InternVL (#45129), InternLM registry alias (#45128).
• Deprecated: First-generation Qwen and QwenVL models (#45131), Transformers v4 support (#45161), CUDA_VISIBLE_DEVICES on ROCm (#46636); general deprecations for v0.23/v0.24 (#44992).

New Contributors

• @abcd1927 made their first contribution in https://github.com/vllm-project/vllm/pull/43098
• @Achyuthan-S made their first contribution in https://github.com/vllm-project/vllm/pull/44795
• @Alex-ai-future made their first contribution in https://github.com/vllm-project/vllm/pull/45905
• @alexbi29 made their first contribution in https://github.com/vllm-project/vllm/pull/45763
• @amanchugh89 made their first contribution in https://github.com/vllm-project/vllm/pull/45840
• @ankrovv made their first contribution in https://github.com/vllm-project/vllm/pull/44608
• @anony-mous-e made their first contribution in https://github.com/vllm-project/vllm/pull/45412
• @appleparan made their first contribution in https://github.com/vllm-project/vllm/pull/45073
• @ashishpatel26 made their first contribution in https://github.com/vllm-project/vllm/pull/43984
• @Bot1822 made their first contribution in https://github.com/vllm-project/vllm/pull/44053
• @ByteFlowing1337 made their first contribution in https://github.com/vllm-project/vllm/pull/45988
• @Change72 made their first contribution in https://github.com/vllm-project/vllm/pull/43756
• @coder3101 made their first contribution in https://github.com/vllm-project/vllm/pull/44801
• @cquil11 made their first contribution in https://github.com/vllm-project/vllm/pull/45720
• @dmaniloff made their first contribution in https://github.com/vllm-project/vllm/pull/40470
• @factnn made their first contribution in https://github.com/vllm-project/vllm/pull/44955
• @FAUST-BENCHOU made their first contribution in https://github.com/vllm-project/vllm/pull/44760
• @felix0080 made their first contribution in https://github.com/vllm-project/vllm/pull/44602
• @gitbisector made their first contribution in https://github.com/vllm-project/vllm/pull/40183
• @gq112 made their first contribution in https://github.com/vllm-project/vllm/pull/43081
• @guan404ming made their first contribution in https://github.com/vllm-project/vllm/pull/35022
• @HanHan009527 made their first contribution in https://github.com/vllm-project/vllm/pull/44528
• @hello-args made their first contribution in https://github.com/vllm-project/vllm/pull/44109
• @HumphreySun98 made their first contribution in https://github.com/vllm-project/vllm/pull/45466
• @j-i-l made their first contribution in https://github.com/vllm-project/vllm/pull/45319
• @JasonLi314 made their first contribution in https://github.com/vllm-project/vllm/pull/45255
• @jeffye-dev made their first contribution in https://github.com/vllm-project/vllm/pull/43595
• @jimmy-evo made their first contribution in https://github.com/vllm-project/vllm/pull/44516
• @jjppp made their first contribution in https://github.com/vllm-project/vllm/pull/45217
• @JOSH1024 made their first contribution in https://github.com/vllm-project/vllm/pull/44784
• @junkang1991 made their first contribution in https://github.com/vllm-project/vllm/pull/46039
• @KaletoAI made their first contribution in https://github.com/vllm-project/vllm/pull/43495
• @kliukovkin made their first contribution in https://github.com/vllm-project/vllm/pull/43724
• @littlecircle0730 made their first contribution in https://github.com/vllm-project/vllm/pull/44750
• @llx-08 made their first contribution in https://github.com/vllm-project/vllm/pull/45357
• @m4r1k made their first contribution in https://github.com/vllm-project/vllm/pull/45795
• @martin-kukla made their first contribution in https://github.com/vllm-project/vllm/pull/45417
• @MichaelCao0 made their first contribution in https://github.com/vllm-project/vllm/pull/46398
• @mrn3088 made their first contribution in https://github.com/vllm-project/vllm/pull/45383
• @nataliepjlin made their first contribution in https://github.com/vllm-project/vllm/pull/45218
• @nehmathe2 made their first contribution in https://github.com/vllm-project/vllm/pull/44912
• @nikhilesh-csa made their first contribution in https://github.com/vllm-project/vllm/pull/45852
• @nv-nedelman-1 made their first contribution in https://github.com/vllm-project/vllm/pull/42120
• @Oseltamivir made their first contribution in https://github.com/vllm-project/vllm/pull/45879
• @parthash0804 made their first contribution in https://github.com/vllm-project/vllm/pull/43844
• @pjdurden made their first contribution in https://github.com/vllm-project/vllm/pull/44942
• @pst2154 made their first contribution in https://github.com/vllm-project/vllm/pull/45181
• @Saddss made their first contribution in https://github.com/vllm-project/vllm/pull/44409
• @sahilsGit made their first contribution in https://github.com/vllm-project/vllm/pull/44499
• @sasindharan made their first contribution in https://github.com/vllm-project/vllm/pull/44383
• @shantipriya-amd made their first contribution in https://github.com/vllm-project/vllm/pull/39498
• @Sirius29 made their first contribution in https://github.com/vllm-project/vllm/pull/46026
• @srajabos made their first contribution in https://github.com/vllm-project/vllm/pull/44665
• @sridhar-3009 made their first contribution in https://github.com/vllm-project/vllm/pull/44055
• @stefankoncarevic made their first contribution in https://github.com/vllm-project/vllm/pull/45706
• @sunnweiwei made their first contribution in https://github.com/vllm-project/vllm/pull/45100
• @TanNgocDo made their first contribution in https://github.com/vllm-project/vllm/pull/44222
• @thisisjimmyfb made their first contribution in https://github.com/vllm-project/vllm/pull/41496
• @tykow made their first contribution in https://github.com/vllm-project/vllm/pull/44663
• @V-3604 made their first contribution in https://github.com/vllm-project/vllm/pull/43362
• @vincentzed made their first contribution in https://github.com/vllm-project/vllm/pull/44930
• @vraiti made their first contribution in https://github.com/vllm-project/vllm/pull/42331
• @wangjiaxin99 made their first contribution in https://github.com/vllm-project/vllm/pull/45794
• @waynehacking8 made their first contribution in https://github.com/vllm-project/vllm/pull/45376
• @x41lakazam made their first contribution in https://github.com/vllm-project/vllm/pull/43300
• @xiaguan made their first contribution in https://github.com/vllm-project/vllm/pull/45286
• @xiaohuguo2023 made their first contribution in https://github.com/vllm-project/vllm/pull/44804
• @xin3he made their first contribution in https://github.com/vllm-project/vllm/pull/43557
• @xx-thomas made their first contribution in https://github.com/vllm-project/vllm/pull/45210
• @yangdian96 made their first contribution in https://github.com/vllm-project/vllm/pull/44173
• @YellowFoxH4XOR made their first contribution in https://github.com/vllm-project/vllm/pull/45057
• @yzhan1 made their first contribution in https://github.com/vllm-project/vllm/pull/44552
• @Zedong-Liu made their first contribution in https://github.com/vllm-project/vllm/pull/45361
• @ZewenShen-Cohere made their first contribution in https://github.com/vllm-project/vllm/pull/41161
• @zhangshuoming990105 made their first contribution in https://github.com/vllm-project/vllm/pull/40912
• @ZiguanWang made their first contribution in https://github.com/vllm-project/vllm/pull/43981
• @zlxi02 made their first contribution in https://github.com/vllm-project/vllm/pull/44595

Contributors

Thank you to everyone who made this release possible!

@yewentao256, @Sunt-ing, @jperezdealgaba, @AndreasKaratzas, @BugenZhao, @sfeng33, @njhill, @micah-wil, @bbrowning, @mgoin, @jeejeelee, @hmellor, @tlrmchlsmth, @xianbaoqian, @mmangkad, @jikunshang, @Dao007forever, @zhenwei-intel, @noooop, @Isotr0py, @ivanium, @reidliu41, @varun-sundar-rabindranath, @chaunceyjiang, @WoosukKwon, @mawong-amd, @zxd1997066, @chaojun-zhang, @NickLucche, @bigPYJ1151, @ZJY0516, @charlifu, @yzong-rh, @divakar-amd, @khluu, @cleonard530, @wseaton, @xiaohongchen1991, @ywang96, @taneem-ibrahim, @mikekg, @itayalroy, @Alex-ai-future, @sahilsGit, @bnellnm, @littlecircle0730, @majian4work, @ricky-chaoju, @ronensc, @Fangzhou-Ai, @lucianommartins, @Srinivasoo7, @zyongye, @Rohan138, @Etelis, @wentian-byte, @ekagra-ranjan, @LucasWilkinson, @tahsintunan, @waynehacking8, @gau-nernst, @tuukkjs, @stefankoncarevic, @Palaiologos1453, @lucifer1004, @jmamou, @liulanze, @Terrencezzj, @Change72, @LopezCastroRoberto, @he-yufeng, @benchislett, @juliendenize, @s3woz, @panpan0000, @ilmarkov, @zixi-qi, @wcynb1023, @fynnsu, @ZhanqiuHu, @yuwenzho, @tdoublep, @MatthewBonanni, @hickeyma, @majunze2001, @mrn3088, @Yejing-Lai, @vllmellm, @Saddss, @DarkLight1337, @hongxiayang, @m4r1k, @qli88, @jonathanc-n, @felix0080, @djramic, @aoshen02, @fxmarty-amd, @simon-mo, @llsj14, @akii96, @walterbm, @dmaniloff, @zlxi02, @grYe99, @jeffye-dev, @parthash0804, @qyYue1389, @sagearc, @maeehart, @TanNgocDo, @cinnamonica02, @zucchini-nlp, @tykow, @mganczarenko, @yangdian96, @jimmy-evo, @YellowFoxH4XOR, @yzhan1, @shenoyvvarun, @yufufi, @laviier, @xiaohuguo2023, @EanWang211123, @JartX, @shantipriya-amd, @askliar, @hallerite, @appleparan, @effi-ofer, @angelayi, @TheCodeWrangler, @DanBlanaru, @ankrovv, @velonica0, @pjdurden, @cyyever, @wjinxu, @kliukovkin, @x41lakazam, @Jasen2201, @r-barnes, @tc-mb, @nataliepjlin, @KaletoAI, @WineChord, @fangyuchu, @vraiti, @nascheme, @jjppp, @sasindharan, @xiaguan, @snadampal, @chfeng-cs, @thillai-c, @guan404ming, @sridhar-3009, @vincentzed, @j-i-l, @rjrock, @abinggo, @anony-mous-e, @Achyuthan-S, @Harry-Chen, @mfylcek, @amd-asalykov, @noa-neria, @maobaolong, @TheEpicDolphin, @FAUST-BENCHOU, @martin-kukla, @xin3he, @ZiguanWang, @youkaichao, @factnn, @llx-08, @xx-thomas, @gitbisector, @Bortlesboat, @thisisjimmyfb, @JOSH1024, @wendyliu235, @wangxiyuan, @shen-shanshan, @HanHan009527, @amd-lalithnc, @netanel-haber, @fuscof-ibm, @AjAnubolu, @carlyou, @abcd1927, @CienetStingLin, @kouroshHakha, @alexbi29, @jesse996, @sungsooha, @andakai, @cquil11, @nehmathe2, @liangel-02, @hello-args, @j9smith, @nikhilesh-csa, @ruocco, @oguzhankir, @yiliu30, @xaguilar-amd, @amirkl94, @danisereb, @wangjiaxin99, @shanjiaz, @Oseltamivir, @alexeldeib, @wzhao18, @coder3101, @lyd1992, @markmc, @ashishpatel26, @HumphreySun98, @ByteFlowing1337, @nv-nedelman-1, @JaredforReal, @sammshen, @okorzh-amd, @muhammadfawaz1, @vadiklyutiy, @JasonLi314, @SumanthRH, @Sirius29, @tjtanaa, @zhangshuoming990105, @amanchugh89, @umut-polat, @srajabos, @junkang1991, @pst2154, @WindChimeRan, @Zedong-Liu, @gq112, @sunnweiwei, @athrael-soju, @EazyReal, @Liangliang-Ma, @jinzhen-lin, @V-3604, @aarushjain29, @ZewenShen-Cohere, @Bot1822, @BowenBao, @MichaelCao0, @tanpinsiang, @QwertyJack, @nagisa-kunhah, @Meihan-chen, @robertgshaw2-redhat