NVIDIA GeForce RTX 5090

### Name and Version llama.cpp version - b9038 . ### Operating systems Windows ### GGML backends CUDA ### Hardware RTX 5090 ### Models Mistral Medium 3.5 128B Q4_K_M mmproj.GGUF ### Prob

{"text":"Mistral Medium 3.5 128B (Q4_K_M) と mmproj.GGUF を使用。CLIPモデルのロード失敗により推論不可。"}

### 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":"Nvidia Studio driver 596.36, ComfyUI portable v0.20.1. CUDA not available error and access violation."}

## Purpose Add encoder CUDA Graph support for MiniCPM-V 2.5, 2.6, 4.0, and 4.5 as part of tracker #38175. This implementation follows the existing workflow introduced in #38061. The captured grap

{"text":"MiniCPM-V-4.5/2.6 models with encoder CUDA Graph enabled on vLLM."}

## Overview Fuse rms_norm+ mul+ qunatize_q8_1 ## Additional information Tested on Qwen-3.6-35B-A3B-Q4_KM model, out of 81 RMS norms 41 are fused and 40 remained unfused due to incompatible Mo

{"text":"Qwen-3.6-35B-A3B-Q4_KM model. Performance boost of 3-5% observed with fused rms_norm, mul, and quantize_q8_1."}

## Overview Fuse rms_norm+ mul+ qunatize_q8_1 ## Additional information Tested on Qwen-3.6-35B-A3B-Q4_KM model, out of 81 RMS norms 41 are fused and 40 remained unfused due to incompatible Mo

{"text":"Qwen-3.6-35B-A3B-Q4_KM model. Performance boost of 3-5% observed with fused rms_norm, mul, and quantize_q8_1."}

If the same weight is used multiple times within the same prefetch window, it should only apply compute state mutations once. Mark the weight as fully resident on the first pass accordingly. Exampl

{"text":"Linux, LTX2.3, LTX2.0 T2V, WAN2.2, and Flux 2. All tests passed including async-offload tests."}

### Name and Version Current head in repo d8794eecd (HEAD -> master, origin/master, origin/HEAD) examples: refactor diffusion generation (#22590) ./build/bin/llama-server --version ggml_cuda_init:

{"text":"Qwen3.5-35B-A3B-Q6_K.gguf を使用。GitHub CoPilot クライアントでのツール呼び出しの出力形式にバグがあり、正常に処理されない。"}

(Urgent) crasher fix for LTX2.3 on RTX5090 or greater. Example test conditions: RTX5090, linux LTX2.3 FP8 <img width="1015" height="571" alt="image" src="https://github.com/user-attachments/a

{"text":"LTX2.3 FP8 on Linux. Fixed a crash (AttributeError) occurring on RTX 5090 or greater."}

### 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":"v6.13.0rc1でFlux.1 DevおよびFlux.2 KleinのLoRAが正常に動作せず、出力が乱れる。v6.12.0では正常動作を確認済み。"}

### 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":"Qwen2511 and 2512 models (GGUF) where VAE/Text_Encoder source is missing."}

### What is the issue? I tried creating a model from safetensors with quantization. I accidentally typed `Q5_K_M` instead of `Q4_K_M`. `ollama.exe` proceeded to: 1. Import all model files into `bl

{"text":"safetensorsからモデルをインポートする際、サポートされていない量子化形式(Q5_K_M)を指定したため失敗。"}

Implement block based prefetch and async offload of lora weights. Adopt this in LTX2 model. Previous draft of block based prefetch here. https://github.com/Comfy-Org/ComfyUI/pull/10594 Back then

{"text":"LTX 2.3 FP16 dev+lora 720Px5s, Linux, PCIe4x16. Performance improved via block prefetch and async offload."}

## Current Status [10 May] As reminded by @orozery, after #37885 is merged, I'll update this PR to override `initialize_worker_connector` in Mooncake, so that when the model runner takes the canonic

{"text":"Qwen/Qwen2.5-0.5B-Instruct, 2 RTX5090, P/D disaggregation with cross-layer enabled."}

## Purpose This PR bumps FlashInfer from the previous version to v0.6.11. This PR may help integrate the FlashInfer B12x MoE and FP4 GEMM kernels for SM120/121. #40082 ## Test Plan 1. After

{"text":"FlashInfer v0.6.11 B12x backend unit tests passed on RTX 5090 (SM120) and GB10 (SM121)."}

## Overview Currently speculative checkpoint needs to restart from a checkpoint after some draft tokens are not accepted, this leads to some wastage in running the target again. This PR adds the ab

{"text":"Qwen3.5-27B-Q4_K_M.gguf (target) and Qwen3.5-0.8B-Q8_0.gguf (draft) with speculative decoding, achieving 126 tokens/sec on PR branch."}

## Overview Implements an internal reduction kernel for tensor parallelism mode. Improved performance over the butterfly fallback, used on Windows until NCCL is supported. ## Additional informat

{"text":"2 x RTX 5090, internal AllReduce kernel for CUDA provider, various models (llama 70B, llama 3B, qwen35moe, qwen3, gemma4) with different quantizations."}

### Custom Node Testing - [ ] 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":"Flux.2 Lora Training workflow on ComfyUI v0.18.1 (WSL), OOM during blockswapping phase."}

### Custom Node Testing - [ ] 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":"Flux.2 Lora Training on ComfyUI v0.18.1 (WSL), OOM during blockswapping phase."}

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

{"text":"vLLMでgemma4モデルを使用し、マルチターン会話でのストリーミング時に推論プロセスがコンテンツに漏洩するバグ報告。"}

## Overview <!-- Describe what this PR does and why. Be concise but complete --> Enable concurrent streams for linear models (e.g. Qwen3.5). Linear attention layers also have parallelizable kern

{"text":"gemma4 and qwen35 27B Q4_K_M models with GGML_CUDA_GRAPH_OPT=1, showing speedup with concurrent streams."}