#llama-cpp

Harnesses

The Hermes Vertical Router on a DGX Spark — One Brain Always Warm, Five Specialists Summoned on Demand

Five published Orionfold verticals plus the pinned MoE brain become a router on one Spark — not by parallel inference (the unified-memory envelope forbids that), but by a deterministic keyword classifier that dispatches the prompt and serves the right specialist one-at-a-time.

uses fieldkit.harness

Harnesses

Picking the Hermes Brain on a DGX Spark — When Throughput Stops Being the Answer

The Hermes serving-lane bakeoff couldn't pick a winner: all five lanes cleared the tool-call format bar. A graded brain-quality rubric breaks the tie — and shows the fastest serving lane is also the better agent, by a margin throughput could never have measured.

uses fieldkit.evalfieldkit.harness

Harnesses

The Hermes Serving Lane on a DGX Spark — MoE vs Dense, and the Number That Actually Picks the Lane

Five Hermes serving lanes on one DGX Spark: Qwen3-30B-A3B MoE vs Qwen3-32B dense across vLLM, llama.cpp, and NIM. The MoE runs ~8.5× faster for the same memory — but the lane is picked by tool-call reliability, which took two config fights to get to 0% everywhere.

uses fieldkit.capabilitiesfieldkit.harnessfieldkit.nim

Machine that Builds Machines

Unsloth on the Spark — When the Train-Time Peak Equals the Base-Load Peak

Six gates clear in one container against the v1 reset: pip install --no-deps preserves the s40 stack, FastLanguageModel loads at 16.94 GB peak, a 100-step LoRA train holds the same envelope, save_pretrained_gguf() emits both quants in 207 seconds end-to-end.

Three-Mode Bracket: Baselining a Reasoning Model Before Fine-Tuning, On One Spark

Before you fine-tune a small reasoning model on a domain bench you need to know where it stands. Three context modes — closed, retrieval, oracle — triangulate the model's ceiling on one Spark, no Judge backend or cluster required.