面向 8×RTX 4090(SM89)的 DeepSeek-V4-Flash-0731 MXFP4 SGLang 实验分支
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Updated
Nov 2, 2026 - Python
面向 8×RTX 4090(SM89)的 DeepSeek-V4-Flash-0731 MXFP4 SGLang 实验分支
4K video super-resolution on a 24 GB RTX 4090 — a free, local ComfyUI FlashVSR node (VIDEO in, VIDEO out) plus a Triton/FP8 operator pack at ~1.3×. No CUDA build, every number backed by benchmark JSONs.
QSA HiSparse as an SGLang plugin: 256K CPU KV offload and text/image host prefix reuse for Qwen Sparse Attention on unmodified SGLang
Run DeepSeek-V4-Flash with 1M-token context on pre-Hopper NVIDIA GPUs (sm_89) — a vLLM 0.26.0 overlay replacing all SM90+ DeepGEMM/FlashMLA operators with Triton/TileLang/Marlin kernels + CPU-KV-pool (pinned host memory via CUDA UVA).
Feeding the Tensor Cores: a dense FP16 GEMM for NVIDIA Ada (sm_89) at 96.5% of cuBLAS, and what it teaches about how each GPU generation handles async copy and Tensor Core issue.
RTX 4090 / Ada SM89-focused NInfer-all fork: CUDA 13.4 builds, device calibration, GGUF IQ3_S profiling, DFlash2/MTP tuning and reproducible benchmarks.
SMO.H3 Studio 的 SelfLift Spark 运行环境分发仓库(SM89 / SM120 运行包与环境清单)
Port NInfer, a single-GPU CUDA inference engine, to the NVIDIA L20 (Ada sm_89, 92 SMs, 48 GB): patch set, build tooling, and measured results
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