NVIDIA GeForce RTX 5070

Fixes #60172 ## Purpose On GPUs whose opt-in shared memory per block is 101376 bytes (consumer Ampere, Ada and Blackwell: sm_86, sm_89, sm_120), `TRITON_MLA` decode fails as soon as the first token

{"text":"vLLM 0.31.0, Triton 3.7.1, DeepSeek-style model (MLA), fp8 KV-cache dtype, sm_120"}

## Overview Fix `CUBLAS_STATUS_INVALID_VALUE` in CUDA `OUT_PROD` when an operand has a transposed size-1 dimension. Transposing a `[1, n]` tensor produces a `[n, 1]` tensor with `nb[1] == sizeof(flo

{"text":"CUDA 13.3, sm_120"}

### 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":"CUDAMallocAsync使用、ComfyUI 0.39.0でKSamplerがクラッシュ"}

## Overview An `rpc-server` on a GPU that is shared with other consumers (desktop session, other apps) will keep allocating device memory for the client's buffers as long as the GPU has free memory:

{"text":"RTX 4070 12GBとRTX 5070の2ノードRPCクラスタで、Qwen3.8-27Bモデルを約60%のレイヤーをワーカーにオフロードして動作確認。メモリ上限設定によりOOM回避と正常なサービングを確認。"}

## Purpose Fixes #58423. A long candidate response can overflow a Chat Completions prompt. `truncate_prompt_tokens` cuts the rendered token sequence and can remove the instructions or rubric needed

{"text":"Qwen2.5-0.5B-Instruct model, RTX 5070 GPU"}

## Overview Fixes #27901, refs #27911. The CUDA norm kernels use `(nrows, nchannels, nsamples)` as the launch grid, but `gridDim.y/z` is limited to 65535. When `ne[2]` or `ne[3]` exceeds that, t

{"text":"CUDA 13.3, tested with 65536 channels/samples"}

## Overview Fixes #27901, refs #27911. The norm kernels launch with grid `(nrows, nchannels, nsamples)`, but CUDA limits `gridDim.y/z` to 65535. When `ne[2]` or `ne[3]` exceeds that, the launch

{"text":"CUDA NORM/RMS_NORM/L2_NORMカーネルのバグ修正検証。RTX 5070上でテストバックエンドスイートがパスし、パフォーマンスも確認された。"}

### 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":"GPUはGeForce RTX 5070、Torchのインストールに失敗し、CPUで動作している"}

### 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":"Linux環境でWindows版ComfyUIをWine経由で実行し、GPU(RTX 5070)での動作に失敗してCPUでのみ動作している。"}

### 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":"Linux環境でWindows版ComfyUIをWine経由で実行し、GPU(RTX 5070)での動作に失敗してCPUでのみ生成可能。"}

### Summary `llama-server` crashes with `CUDA error: an illegal memory access was encountered` in `ggml_backend_cuda_synchronize` (`cudaStreamSynchronize`, `ggml-cuda.cu:2485` on b10107 / `:2499` on

{"text":"Qwen3.6-35B MoEモデルとflash-attn有効時、2番目のリクエストでCUDA illegal memory accessが発生。flash-attn無効化で解消。"}

### Summary `llama-server` crashes with `CUDA error: an illegal memory access was encountered` in `ggml_backend_cuda_synchronize` (`cudaStreamSynchronize`, `ggml-cuda.cu:2485` on b10107 / `:2499` on

{"text":"Qwen3.6-35B MoE (Q4_K_M) 使用。flash-attn有効時にCUDA illegal memory accessでクラッシュし、無効化すると動作する。"}

### Summary `llama-server` crashes with `CUDA error: an illegal memory access was encountered` in `ggml_backend_cuda_synchronize` (`cudaStreamSynchronize`, `ggml-cuda.cu:2485` on b10107 / `:2499` on

{"text":"Qwen3.6-35B MoEモデルでのflash-attn有効時、2番目のリクエストでCUDA illegal memory accessが発生。--flash-attn offで回避可能。"}

### Summary `llama-server` crashes with `CUDA error: an illegal memory access was encountered` in `ggml_backend_cuda_synchronize` (`cudaStreamSynchronize`, `ggml-cuda.cu:2485` on b10107 / `:2499` on

{"text":"Qwen3.6-35B MoE model with partial expert offload, CUDA flash-attention enabled"}

### Name and Version - llama.cpp: `b10194-e1a1abb78` - OS: Ubuntu 24.04 - GPU: NVIDIA GeForce RTX 5070 - Backend: CUDA - Models tested: - KAT-Coder-V2.5-Dev-MTP - Ornith-1.0-35B-MTP - Qwen3.6-3

{"text":"llama-serverでMTPモデルと--spec-type draft-mtp、--embeddingを併用した際にGGML_ASSERTエラーで起動失敗"}

### Name and Version - llama.cpp: `b10194-e1a1abb78` - OS: Ubuntu 24.04 - GPU: NVIDIA GeForce RTX 5070 - Backend: CUDA - Models tested: - KAT-Coder-V2.5-Dev-MTP - Ornith-1.0-35B-MTP - Qwen3.6-3

{"text":"MTPモデルにて--spec-type draft-mtpと--embeddingを併用するとGGML_ASSERTでクラッシュする。"}

## Overview Fixes #25568. This PR fixes incorrect results and backend failures for `SUM` on non-contiguous tensors across the CPU implementation, CUDA capability checks, and backend tests. Th

{"text":"CUDA 13.3環境下で非連続テンソルのSUM演算が正しく実行され、バックエンドテストが全てパスした。"}

## Overview Fixes #25568. This PR fixes incorrect results and backend failures for `SUM` on non-contiguous tensors across the CPU implementation, CUDA capability checks, and backend tests. Th

{"text":"CUDA 13.3環境にて、non-contiguous tensorsに対するSUM演算の修正を確認。テストケース 12989/12989 全てパス。"}

## Overview Fixes #25568. This PR fixes incorrect results and backend failures for `SUM` on non-contiguous tensors across the CPU implementation, CUDA capability checks, and backend tests. Th

{"text":"CUDA 13.3環境にて、non-contiguous tensorsに対するSUM演算の修正を確認。テストケースはすべてパスしている。"}

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{"text":"GLM-4.7-Flash Q4_K_XLモデル、q4_0 KVキャッシュ使用時にb9868-b9890間で約7-9%のパフォーマンス劣化が発生"}