The GPU efficiency harness
Galahad installs on the GPU servers you already rent and makes the same hardware do multiples of the work. Training finishes 4–5× faster. Long contexts load 8.8× faster — bit-exact. Repeated queries cost 82% less.
every number below is measured · sha-256 verified · invoice on request
galahad train --nodes 8xH100 --tokens 15B› eager baseline .......... 24–36 h (~€720)› with harness ............ 6–8 h (~€180)taliesin graft --ctx 64k› fresh prefill ........... 90.6 s› graft restore ........... 10.3 s (8.8×)› logits sha-256 .......... identical ✓galahad query --repeat› cost vs plain RAG ....... −82% (break-even @ query 2)One harness, three modules
License the Full Stack — or start with one module. Separately they always cost more than the bundle: the harness is the optimal setup.
alone: 50% of RunPod rate
Custom CUDA engine that collapses kernel-launch overhead. Launch-bound training runs become compute-bound: the same 15B-token run drops from ~30 hours to 6–8 on an 8×H100 node.
own production runs: ~€720 → ~€180 per run
alone: 30% of RunPod rate
Lossless model memory. Computed state is saved once and grafted back bit-exactly — the expensive prefill that plain RAG repeats on every query is paid a single time. Not a RAG replacement: optimised RAG.
8.8× faster at 64K context · sha-256 identical · break-even from query 2
alone: 15% of RunPod rate
Data deduplication at GB/s. Strips redundant context before it reaches your model — 22% on agent sessions, up to 71% in RAG pipelines. Pay per token? Merlin cuts the bill from day one.
published methodology: 22% agent sessions, up to 71% RAG
Fleet, utilisation, workload fit. Wrong fit? We say so and part as friends.
Up to 3 months, 2 servers, 50% off — with a measurement protocol agreed up front.
Your data never leaves your infrastructure. Cancel per server, monthly, after the first year.
Per-server licensing · indexed to public RunPod rates · pilots that measure before you commit