NVIDIA GeForce RTX 5080

## Overview Fixes #60261. On a host with no CUDA toolkit, vLLM picks DeepGEMM for block-FP8 linear layers. DeepGEMM's JIT then asserts in `process_weights_after_loading`. With this PR, `is_deep_gem

{"text":"CUDA toolkitなし環境でQwen3-0.6B-FP8モデルのロードに成功"}

Fixes #16754. `CheckpointFunction` captures only CUDA autocast, so backward recomputes CPU and XPU forwards in float32 after their autocast context exits. Capture the forward settings for CPU, CUDA a

{"text":"PyTorch 2.11.0環境でCPUとRTX 5080 Laptop GPUを用いたユニットテストが25件成功し、既存の動作確認テストも全てパスした。"}

`QwenImage21TransformerBlock` clamped fp16 activations with `Tensor.clip`, which returns a new tensor. That allocation is recorded by the memory compiler, and `malloc_graph_pop` fails: ``` RuntimeErr

{"text":"Qwen Image 2.1 turbo, 1248×1664, 8 steps, --fp16-unet --fast fp16_accumulation, memory compiler enabled"}

`QwenImage21TransformerBlock` clamped fp16 activations with `Tensor.clip`, which returns a new tensor. That allocation is recorded by the memory compiler, and `malloc_graph_pop` fails: ``` RuntimeErr

{"text":"Qwen Image 2.1 turbo, 1248×1664, 8 steps, --fp16-unet --fast fp16_accumulation, memory compiler enabled"}

### Proposal to improve performance On #54655, @njhill suggested evaluating "whether we want to change the default of [`VLLM_WSL2_ENABLE_PIN_MEMORY`] generally." Here's that evaluation on one WSL2 ma

{"text":"WSL2環境でQwen3-8B-AWQモデルを使用し、ピンメモリ有効化によりデコード速度が約12%向上した"}

### Is there an existing issue for this problem? - [x] I have searched the existing issues Related but not the same: #9147 / PR #9572 (Qwen-Image encoder VRAM accounting), #9257 (partial loading bud

{"text":"Z-Image Turbo bf16 + LoRA使用時、VRAMオーバーフローにより共有メモリへページングが発生し、生成時間が約30分まで劣化。"}

### Is there an existing issue for this problem? - [x] I have searched the existing issues Related but not the same: #9147 / PR #9572 (Qwen-Image encoder VRAM accounting), #9257 (partial loading bud

{"text":"Z-Image Turbo bf16 + LoRA使用時、VRAM不足によりシステムメモリに溢れ、生成に約30分かかる重大な性能劣化が発生"}

Currently this is too conservative in two ways: - It requires the VRAM to be free at the time of the check to use VRAM mode which gets disrupted by smart memory + dyn vram - It requires 4x RAM to

{"text":"Windows環境、32GB RAM、Qwen 2.1モデル使用。KVキャッシュロジック改善により、VRAM/RAMモードの切り替えが最適化され、処理速度が向上した。"}

### Name and Version `llama-cpp-python` 0.3.35 (prebuilt CUDA wheel from `https://abetlen.github.io/llama-cpp-python/whl/cu124`, installed via `pip install llama-cpp-python --extra-index-url https://

{"text":"Qwen-AgentWorld-35B-A3B (MoE) モデルのCPU推論時にSIGILLエラーが発生。AMD Zen5 CPUでAMX_INT8が誤検出されている可能性がある。"}

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。"}

### Name and Version [b10766](https://github.com/ggml-org/llama.cpp/releases/tag/b10766) On NVIDIA driver 610.88 with RTX 5080 CUDA 13.3 build performs worse than Vulkan Build in qwen 3.8 27B I'm not

{"text":"NVIDIA driver 610.88環境下で、CUDA 13.3ビルドがVulkanビルドより性能が劣る（CUDA: 16t/s vs Vulkan: 35.59t/s）。モデルはQwen3.8-27B (IQ4_XS量子化)。"}

### 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。"}

### Name and Version llama-server --version version: 10154 (0e4a03622) built with GNU 13.3.0 for Linux x86_64 ### Operating systems Linux ### GGML backends CUDA ### Hardware Ryzen 7950x3d, RAM

{"text":"MoEモデル(Qwen3.5 35B-A3B Q6_K)をVRAMに完全には収められず、--n-cpu-moe使用時にfused GDN最適化が無効化され速度が低下(40 t/s以下)するバグの報告。修正後は70+ t/sに回復。"}

### Name and Version llama-server --version version: 10154 (0e4a03622) built with GNU 13.3.0 for Linux x86_64 ### Operating systems Linux ### GGML backends CUDA ### Hardware Ryzen 7950x3d, RAM

{"text":"MoEモデル(Qwen3.5 35B-A3B Q6_K)をVRAMに完全には収められない状態で、fused GDN最適化が無効化され速度が低下(40 t/s以下)するバグの報告と修正後の改善(70+ t/s)を含む。"}

## 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。"}

### Git commit Release binaries b10354 and b10358 (`llama-*-bin-win-cuda-13.3-x64.zip`), Windows x64 ### Operating systems Windows ### GGML backends CUDA ### Hardware RTX 5080 16GB (compute 12.

{"text":"EAGLE3 speculative pairing with gpt-oss-20b model on Windows CUDA backend fails to load draft model with 'invalid vector subscript' error, while standalone loading works."}

### Git commit Release binaries b10354 and b10358 (`llama-*-bin-win-cuda-13.3-x64.zip`), Windows x64 ### Operating systems Windows ### GGML backends CUDA ### Hardware RTX 5080 16GB (compute 12.

{"text":"EAGLE3 speculative pairing with gpt-oss-20b-MXFP4 target model on Windows CUDA backend fails to load draft model."}

## Summary On Linux with dual NVIDIA Blackwell GPUs (RTX 5080 + RTX 5070 Ti, CUDA 13.3), `llama-server` silently ignores the second GPU and loads the entire model onto GPU0, causing an OOM when the m

{"text":"Linux環境でRTX 5080とRTX 5070 TiのデュアルGPU構成を使用し、llama-serverでのマルチGPUテンソル分割が機能せずOOMエラーが発生した。"}