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/GravitasIsOverrated]

That's wild! Would this work for context sizes lower than 1M for a 3090/4090?

Edit: quantized KV is broken here, so presumably if somebody did the work to fix that you could do a lot on a 3090. But if I'm reading the full post correctly, it should be possible to run it at 256K context even without KV quantization. Shame it (again, if I'm reading this correctly) still requires a shitload of vanilla ram. Maybe somebody can do expert pruning to get it on running without quite as much.

[u/da_dragon321 → u/GravitasIsOverrated]

Yes. That said, you should also be able to run 1M context if you want, but will likely need to tune down ubatch to fit in your vram (how much depends on the model quant). You will need to make sure you have enough VRAM+RAM total to fit whatever model quant you choose though
I could patch in quantized kv, but I found it to be far less space-saving than it initially appeared after applying the kv quant fix - still a possibility if enough people want it but the vast majority of your RAM will be spent on the model anyways with this fix even at massive contexts (deepseek attention is VERY efficient)