NVIDIA GeForce RTX 5080

https://github.com/Comfy-Org/ComfyUI/issues/16144 There are some memory allocations in sparse attention that live across attention blocks or steps that don't get freed during the lifetime of the tr

{"text":"Windows環境でMiniMax H3モデルとtopk sparse attentionを使用し、I2V（画像から動画生成）タスクを実行。VRAMリークが修正され、グラフブレイクが752から32に減少、実行時間が40.43秒から35.99秒に短縮された。"}

### What is the issue? After the latest update the Ollama GUI is WHITE and not following the Windows dark-mode setting (And there is no user-setting in Ollama to switch it to dark-mode) ### Relevant

{"text":"Ollama GUIがWindowsのダークモード設定を反映せず、完全に白背景になるバグが発生"}

### What is the issue? On ollama 0.32.9 (Windows, GGUF, `POST /api/chat`), five models on one server, none of them setting `num_ctx`, split into two behaviours on the same over-length prompt. I can't

{"text":"ollama 0.32.9環境で、一部モデルはHTTP 400エラー、他はHTTP 200だがプロンプトが2050トークンで切り詰められる"}

https://github.com/Comfy-Org/ComfyUI/issues/15255 Default to just the first GPU when multiple nvidia GPUs are detected. This is a workaround to a confirmed at-large in Cuda when multiple GPUs are i

{"text":"Windows環境でRTX 5080とRTX 5060を併用し、Flux2モデルを実行。マルチGPU初期化によるCUDAメモリ不足エラーを回避するため、デフォルトで最初のGPUのみを使用する設定に変更した。"}

## Summary When a workflow containing `MiniMaxH3ReferenceToVideo` is submitted through the HTTP API (`POST /prompt`) rather than built in the browser, the reference inputs have **no effect on the ou

{"text":"MiniMax-H3モデルを使用。API経由でリクエストした際、参照画像・音声が無視され出力に反映されない。"}

### Custom Node Testing - [x] I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-wi

{"text":"Minimax H3 workflow. Generation time increased from 12 minutes to over 25 minutes after updating to version 0.31."}

## Purpose Inkling folds the muP width divisor into a bespoke lm_head `torch.addmm` that emits logits in the input (bf16) dtype, silently dropping an fp32 `head_dtype` (`--hf-overrides '{"head_dtype"

{"text":"Inklingモデルのfp32 head_dtypeにおけるmuPの畳み込み処理を検証し、ビットレベルで同一であることを確認。"}

## Purpose Models with a clamped SwiGLU-OAI MoE activation (`swigluoai_uninterleave`, e.g. [nvidia/MiniMax-M3-NVFP4](https://huggingface.co/nvidia/MiniMax-M3-NVFP4)) cannot use the FlashInfer b12x Mo

{"text":"FlashInfer #3744 merge commit使用時、swigluoai activationを含むMoEモデルのテストが成功。"}

### 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":"InvokeAI v6.12.0にて、ワークフロー実行時にLoraコレクションが機能しないバグ報告。"}

### 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":"InvokeAI v6.12.0, Windows, Lora collection not firing during generation on workflow"}

### Your current environment ### Environment - vLLM: `0.20.2rc1.dev35+g4845aee6b` - Python: `3.12.13` - Torch: `2.11.0+cu130` - CUDA driver/runtime: NVIDIA driver `595.71.05`, CUDA `13`

{"text":"Qwen3.5-9B (NVFP4量子化) を TurboQuant KV cache および chunked prefill 有効で動作させた際、大きなプロンプトの継続処理でクラッシュ。"}

### Your current environment ### Environment - vLLM: `0.20.2rc1.dev35+g4845aee6b` - Python: `3.12.13` - Torch: `2.11.0+cu130` - CUDA driver/runtime: NVIDIA driver `595.71.05`, CUDA `13`

{"text":"Qwen3.5-9B (NVFP4 quantization) with TurboQuant KV cache and chunked prefill enabled."}

### Git commit COMPILED, BUT CAN'T RUN ON RTX5080 GPU：RTX 5080（Blackwell） DRIVER：591.86 CUDA：13.1 / NVCC 13.2 CMake：GGML_CUDA=ON, CMAKE_CUDA_ARCHITECTURES=120 LOG：mmq_x_best=0 + mmq.cuh:4135: fatal

{"text":"CUDA 13.1, CMAKE_CUDA_ARCHITECTURES=120. Fatal error occurred in mmq.cuh during runtime."}

### Custom Node Testing - [x] I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-wi

{"text":"Out of memory error. Device limit: 15.92 GiB."}

### Name and Version build that I start llama-server from: ``` ggml_cuda_init: found 1 ROCm devices: Device 0: AMD Radeon RX 6900 XT, gfx1030 (0x1030), VMM: no, Wave Size: 32 version: 7917 (91ea44e

{"text":"GLM-4.7-Flash-MXFP4_MOEを使用。Flash Attention有効時にGGML_ASSERTエラーで失敗する。"}

### Name and Version build that I start llama-server from: ``` ggml_cuda_init: found 1 ROCm devices: Device 0: AMD Radeon RX 6900 XT, gfx1030 (0x1030), VMM: no, Wave Size: 32 version: 7917 (91ea44e

{"text":"GLM-4.7-Flash-MXFP4_MOE.gguf with flash attention on, KV cache quantization (q4_0) enabled. Fails with GGML_ASSERT(max_blocks_per_sm > 0) on RTX 5080."}