fraQtl Membrane Runtime for llama.cpp (prebuilt, CUDA)
Prebuilt binaries of the fraQtl compression membrane integrated into a llama.cpp fork: the KV cache is stored in calibrated compressed pages and the attention kernel reads them directly at tensor-core speed β no decompress-then-attend stage. At long context, where decode is bandwidth-bound, reading fewer bytes makes generation faster.
Measured (receipts on the model cards): 1.79Γ faster than q8_0 KV at 128K decode on Qwen3-4B (92% of fp16 speed at ~2.45Γ less KV memory), 36 vs 28 users before OOM at 32K/user β and on Mistral-Nemo-12B, 32 vs 24 users, second model with zero code changes.
Platform
- Linux x86_64 Β· CUDA 12.4 Β· SM80 + SM90 (A100 / H100 class). This is the server/workstation lane β no macOS/ARM build.
- Built 2026-08-24 (
MANIFEST.jsonin this repo carries per-file sha256 β verify your download against it).
Files
llama-server, llama-cli, llama-fraqtl-niah-parallel (the retrieval-gate
harness) and 7 shared libraries (libllama, libllama-common, libggml,
-base, -cpu, -cuda, libmtmd). Kernel source is not distributed β same
posture as our vLLM runtime wheel;
the binaries plus published sidecars are sufficient to run and verify every
number on the model cards.
How to run
export FRAQTL_MEMBRANE=1 FRAQTL_MEMBRANE_EXCLUSIVE=1
LD_LIBRARY_PATH=. ./llama-server -m <model>.gguf \
--fraqtl-kv --fraqtl-eigenbasis <v-sidecar>.bin \
--fraqtl-kv-protect 32 --fraqtl-k-eigenbasis <k-sidecar>.bin \
--fraqtl-sink-tokens 0 --fraqtl-residual-window 0
Calibrated sidecars per model: Qwen3-4B-Instruct-2507 Β· Mistral-Nemo-Instruct-2407
License and credit
This runtime is a fork of llama.cpp (MIT β license included; upstream commit pinned in the receipts). The membrane kernels are fraQtl's; llama.cpp and its contributors are credited at their best β the engine this builds on is excellent.
More from fraQtl
Same membrane, independently implemented in vLLM: nine concurrent β128K users on one A100, 134.1 tok/s, 9/9 retrieval β receipt on the Qwen3-4B sidecar card. Calibration-aware Hi-Fi GGUFs (pair well with this runtime): org page.