Series
Foundations
The shared install path that sets up the substrate the application arcs build on. Plus the bridge piece that names the fork.
Your First NIM on a DGX Spark — What 24.8 Tokens Per Second Doesn't Tell You
First-contact notes on NVIDIA's DGX-Spark-specific Llama 3.1 8B NIM. 9.4 GB image, ~108 s warm-cache cold-start, 24.8 tok/s steady, OpenAI-compatible on :8000 — and a confidently wrong Python one-liner that clarifies what small-model FP8 buys and what it costs.
uses fieldkit.nim
Your Own Semantic Space — a Nemotron Embedding NIM on a DGX Spark
The embedding endpoint that every downstream RAG, wiki, and agent piece will reuse — a 2048-dim Nemotron Retriever NIM running locally on GB10, ready 52 seconds after docker run and holding 28 docs/s under batched load.
uses fieldkit.rag
Where Your Vectors Live — pgvector on a DGX Spark
The substrate between the embed call and the retrieve call — pgvector 0.8.2 running as a Postgres 16 container on GB10, with 1000 Nemotron vectors, HNSW and ivfflat both indexed, and a planner that prefers seq scan until you tell it otherwise.
uses fieldkit.rag
Three Endpoints, One Answer — Naive RAG on a DGX Spark
Three endpoints in one curl chain — a query embeds through Nemotron, pgvector returns top-5 chunks in under 80 ms, and a Llama 3.1 8B NIM stuffs them into a strict-context prompt. The chain works; the 8B generator still refuses on questions its own context answers.
uses fieldkit.ragfieldkit.eval
Hybrid Retrieval on the Spark — BM25, Dense, Fusion, Rerank
Four retrieval modes on one corpus — naive dense, BM25, Reciprocal Rank Fusion, Nemotron rerank. Dense is already 92% recall@5; rerank adds a point at K=10 and reorders the top. The 8B generator still refuses where retrieval is perfect — grounding, not retrieval, is the new bottleneck.
uses fieldkit.rag
Bigger Generator, Same Grounding — 8B vs 49B vs 70B on One Retrieval Chain
The rerank-and-fusion article bet that a bigger generator would heal the 8B Google-IPO refusal. Ran the A/B across three sizes on one retrieval chain. Bet lost: Nemotron-Super-49B over-refuses the 8B baseline; Llama 3.3 70B narrows the gap, not closes it. The refusal was the scaffold working.
uses fieldkit.rag
One Rail, Three Policies — NeMo Guardrails on the Retrieval Path
NeMo Guardrails drops a policy gate between retrieval and generation. One install, three per-arc configs — PII for Second Brain, style for LLM Wiki, code-safety for Autoresearch — and a 15-query benchmark: 100% block recall, 100% clean pass. Rails are scaffolding; detectors are the content.
uses fieldkit.rag
One Substrate, Three Apps — Where the Foundation Forks
Seven articles installed one stack on the Spark — NIM, Embed, pgvector, RAG glue, reranker, generator A/B, Guardrails. This bridge retells that install as three different answers to one question — corpus plus 128 GB — and walks readers to the top of three tracks.