llamacpp patch - DeepSeek V4 Flash running with full 1M token context locally on RTX 5090

Wanted to try running DeepSeek V4 Flash locally but found it asking for absurd amounts of VRAM at higher context lengths (~256GB at 1M). Turned out the DSA lightning indexer lacks proper llamacpp support. Did a bit of digging and there's an upstream PR to address the issue (shoutout u/fairydreaming, PR #24231), but even there it's not wired into the model graph and has no CUDA path yet. So I wired it in and patched a CUDA kernel this morning and figured I'd share in case it's useful to anyone else looking to run something like this. Hardware: RTX 5090, 9950X3D, 96GB DDR5 Model: DeepSeek-V4-Flash, mixed Q8/Q4/Q2 quant by antirez Before / after (256K context): Before After Compute buffer ~67 GiB (OOM) 3.2 GiB Prefill 56 t/s ~263 t/s Decode ~14 t/s ~14 t/s 1M context impossible (~256GB) works (3.75 GiB at ubatch 768, ~6gb at 2048) Validated presets: Context Prefill Decode Peak VRAM 256K ~263 t/s 14 t/s ~29 GiB 512K 256 t/s 13.7 t/s ~28 GiB 1M 159 t/s* 13.7 t/s ~31 GiB *lower ubatch on 32gb 5090 at 1M - should be ~full speed if given the full ~9gb vram Correctness: verified briefly with a needle-in-haystack test - planted a random fact at 10%/50%/90% depth in a 100K-token document, model retrieved it correctly every time. Also retrieved correctly at 512K and 1M's harder 50% depth. Source + build instructions + full writeup: https://github.com/spencer-zaid/llama.cpp/blob/deepseek-lid-cuda/docs/deepseek-v4-lid-cuda.md Branch: https://github.com/spencer-zaid/llama.cpp/tree/deepseek-lid-cuda No prebuilt binary (single GPU tested RTX 5090). Build instructions in the doc in case you need them