NVIDIA GeForce RTX 5070 Ti

### Name and Version version: 0.4.0-dev (build 10825, commit 9e0e22059) built with GNU 13.3.0 for Linux x86_64 ### Operating systems Linux ### Which llama.cpp modules do you know to be affected?

{"text":"Qwen3.8-27B (Q5_K_M) を2枚のRTX 5070 Tiに分割配置しようとしたが、tensor splitの粒度問題によりOOMが発生"}

## Overview Adds a brief note to the -ctkd and -ctvd docs, pointing out that quantization of the MTP cache can unintuitively increase memory usage. Users of llama.cpp on low-VRAM systems are very f

{"text":"Qwen3.8-27B (Q3_K_XL) モデルを 16GB VRAM で動作させ、MTP KV キャッシュの量子化がメモリ使用量を増加させることを確認。"}

## Overview Previous guard caused multi-GPU to skip the graph optimization. The graph is already split per device and the optimization doesnt run over the whole model but once per split, and thus

{"text":"RTX 5070 Ti と RTX 3080 Ti のマルチGPU環境で、Gemma-4-26B (MoE) および Qwen3.8-27B (dense) モデルの推論が成功し、並列ストリーム有効化により MoE モデルで tg 速度が約 3.7% 向上した。"}

### Your current environment <details> <summary>The output of <code>python collect_env.py</code></summary> ```text Collecting environment information... ============================== System

{"text":"vLLM環境で量子化済み埋め込みが多くのモデルタイプで失敗するバグ報告"}

### Name and Version Both machines: version: 0.3.0-dev (build 10675, commit 90c26fcd4) built with Clang 20.1.8 for Windows x86_64 NVIDIA Game Ready Driver 610.88, CUDA 13.3 ### Operating systems W

{"text":"Flash-attention有効かつRPCオフロード時、KVキャッシュ量子化(q8_0/q4_0等)でクラッシュ"}

### Your current environment <details> <summary>The output of <code>python collect_env.py</code></summary> ```text ============================== System Info ============================== O

{"text":"vLLM 0.28.0起動時にホストメモリを全て消費しフリーズする（0.27.1では正常動作）"}

### Is there an existing issue for this problem? - [x] I have searched the existing issues ### Install method Invoke's Launcher ### Operating system Windows ### GPU vendor Nvidia (CUDA) ### GP

{"text":"Flux.2およびSDXLのControl Adapter使用時、黒画像がマスクとして無視され、600px未満の画像が参照画像として機能しない。"}

### Is there an existing issue for this problem? - [x] I have searched the existing issues ### Install method Invoke's Launcher ### Operating system Windows ### GPU vendor Nvidia (CUDA) ### GP

{"text":"Multi-GPU環境（RTX 5070 Ti, RTX 3070 Ti, RTX 3070）でInvokeAI v6.14.0を使用し、l2i/post-denoise処理中に間歇的にストールが発生する。"}

### Name and Version version: 0.1.2-dev (build 10549, commit b2e5e9b28) built with Clang 20.1.8 for Windows x86_64 ### Operating systems Windows ### GGML backends CUDA ### Hardware 14600k + dua

{"text":"Gemma-4-31B-it (Q6_K_XL) と MTP draft model を使用し、flash-attn 有効状態でランダムにクラッシュ"}

### Summary `llama-server` (router mode with `--models-dir`/`--models-preset`) crashes with **0xC0000409 (STATUS_STACK_BUFFER_OVERRUN, fast-fail)** when a second HTTP request hits the single busy slo

{"text":"Qwen3.8-27B-UD-IQ4_XSモデルでxhigh推論時に、バビースロットへの同時リクエストで0xC0000409エラーによりクラッシュ"}

**Summary** Since v0.32.0, MiniMax H3 video generation at full resolution (1280x736, 362 frames, 20 steps) is ~4x slower than in 0.31.1: ~26 min -> an estimated ~2 hours. Small resolutions (e.g. 512x

{"text":"MiniMax H3 (int8/fp8) でフル解像度生成時、v0.32.0以降で速度が約4倍低下（26分→約2時間）。"}

**Summary** Since v0.32.0, MiniMax H3 video generation at full resolution (1280x736, 362 frames, 20 steps) is ~4x slower than in 0.31.1: ~26 min -> an estimated ~2 hours. Small resolutions (e.g. 512x

{"text":"MiniMax H3モデル(int8/fp8)でのフル解像度(1280x736)動画生成がv0.32.0以降、約4倍遅延する回帰バグが発生"}

**Summary** Since v0.32.0, MiniMax H3 video generation at full resolution (1280x736, 362 frames, 20 steps) is ~4x slower than in 0.31.1: ~26 min -> an estimated ~2 hours. Small resolutions (e.g. 512x

{"text":"ComfyUI v0.32.0以降でMiniMax H3のフル解像度動画生成が約4倍遅く、GPU電力消費が低下しメモリ帯域幅が3%まで落ちている"}

### Name and Version ./build/bin/llama-cli --version version: 10284 (9a688e51e) built with GNU 15.3.0 for Linux x86_64 just some random git version from bisecting ### Operating systems Linux ###

{"text":"Qwen3.6-35B-A3B-Q4_K_Mを使用。--fit-target 1024設定時に速度低下（リグレッション）が発生。"}

## Overview Support mixed-precision compressed-tensors checkpoints that combine per-channel FP8 and packed NVFP4 weights. This enables conversion of `unsloth/Qwen3.8-27B-NVFP4`, whose attention and o

{"text":"Qwen3.8-27B-NVFP4モデルを使用し、128KコンテキストおよびQ4_0 KVキャッシュで動作確認済み。"}

## Overview Support mixed-precision compressed-tensors checkpoints that combine per-channel FP8 and packed NVFP4 weights. This enables conversion of `unsloth/Qwen3.8-27B-NVFP4`, whose attention and o

{"text":"unsloth/Qwen3.8-27B-NVFP4 モデルのコンバージョンと、RTX 5070 Ti での 128K コンテキスト生成が成功。"}

### Name and Version version: 0.1.0-dev (build 10428, commit 885c5bbe8) built with Clang 20.1.8 for Windows x86_64 b10428 Windows x64 (CUDA 13) ### Operating systems Windows ### GGML backends CU

{"text":"Qwen3.8-27B-UD-Q4_K_XLモデルで--split-mode tensorと--spec-type draft-mtpを併用した際にCUDAロックアップが発生"}

### Name and Version version: 0.1.0-dev (build 10428, commit 885c5bbe8) built with Clang 20.1.8 for Windows x86_64 b10428 Windows x64 (CUDA 13) ### Operating systems Windows ### GGML backends CU

{"text":"Qwen3.8-27B-UD-Q4_K_XLモデルで--split-mode tensorと--spec-type draft-mtpを併用した際にCUDAロックアップが発生"}

I'm testing LTX-2.5 Distilled using the official/new ComfyUI Text to Video (LTX-2.5) workflow. First, thank you for releasing LTX-2.5. The generation speed and visual quality have been very impressiv

{"text":"LTX-2.5 Distilled (1280x720) で動作。生成速度と品質は良好だが、Prompt Enhancement有効時にプロンプト不整合が発生。"}

## Description After PR #15486 ("Fix peak memory issue with H3", commit `62b3c94b`), H3 long-video generation on a 16 GB GPU regresses sharply: peak VRAM climbs from ~14.5 GB to ~15.6 GB, pushing uti

{"text":"MiniMax H3 (720p/15s/24fps). PR #15486 caused VRAM increase to 15.6GB, triggering WDDM spill and severe slowdown."}