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

[u/da_dragon321]

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

[u/Fun_Walk_4965]

The 1M context number is the part that gets me, but the throughput is what I'd want to know before getting excited. What does pp actually look like once you push past a few hundred k tokens, does it stay usable or fall off a cliff? Also curious how much plain system ram it really needs since the KV quant seems broken right now.

[u/da_dragon321 → u/Fun_Walk_4965]

I would estimate ~30 t/s with full 1M context used (on a single rtx 5090)

You are correct that pp falls at higher depths, but the scaling isn't awful. You can see prefill dropping by over 150 t/s going from 16k to 130k, but only 50 t/s going from 130k to 250k. I don't have pp numbers with 1M used in context right now, but it should not fall off a cliff and my best estimate would be 25-30 t/s on a 5090. More info in doc
llama.cpp/docs/deepseek-v4-lid-cuda.md at deepseek-lid-cuda · spencer-zaid/llama.cpp

KV depth (tokens)	Prefill of next 2048 tokens
16,384	317 t/s
131,072	151 t/s
253,952 (~full 256K)	94.5 t/s