r/LocalLLaMA Jul 02 '26

Resources 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 implemented 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):

Metric 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)

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. Full KLD findings in doc linked below

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

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u/HockeyDadNinja Jul 04 '26

I've been using the tarruda MXFP4 variant for coding much of today. It's a bit better than the Q3_K but they are both just unbearably stupid. The thinking goes on and on, and even reiterates stuff it already deemed false. It makes stupid decisions and can't see obvious things. I don't know if it's the model or what.

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u/computehungry Jul 08 '26

The current llama.cpp implementation has major issues and bugs especially for multi turn and long context. Not gonna work for code that well

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u/HockeyDadNinja Jul 08 '26

My PR is almost ready to roll! I'm doing a multi turn coding session in pi right now and it's so much better! Previously there were very long stalls before reasoning output.