Stage

Deployment

From experiment to something that runs reliably. Containers, services, updates, graceful degradation on one machine.

Article №40 deployment llama.cpp ~5 hours end-to-end on a DGX Spark
Machine that Builds Machines

Orionfold/II-Medical-8B-GGUF on Spark — five medical-reasoning variants, MedMCQA mini-eval, ChatML reasoning format

Five GGUF variants of Intelligent-Internet/II-Medical-8B (Qwen3-8B + DAPO reasoning recipe) measured on a DGX Spark. Q5_K_M lands at 36.4 tok/s, 5.45 GB, and 52% on a MedMCQA n=50 mini-eval — above F16. First reasoning recipe in the series.

uses fieldkit.quantfieldkit.publishfieldkit.evalfieldkit.lineage

Article №39 deployment llama.cpp ~5 hours end-to-end on a DGX Spark
Machine that Builds Machines

Orionfold/SecurityLLM-GGUF on Spark — five cyber variants, CyberMetric mini-eval, MCQ letter scoring

Five GGUF variants of ZySec-AI/SecurityLLM measured on a DGX Spark — Q4_K_M scores 40% on CyberMetric MCQ at 47.7 tok/s and 4.1 GB; the smaller variants matched or beat F16's 34%. Third vertical card; zero fieldkit source changes.

uses fieldkit.quantfieldkit.publishfieldkit.evalfieldkit.lineage

Article №38 deployment llama.cpp ~5 hours end-to-end on a DGX Spark
Machine that Builds Machines

Orionfold/Saul-7B-Instruct-v1-GGUF on Spark — five legal variants, LegalBench mini-eval, four-axis measurement card

Five GGUF variants of Equall/Saul-7B-Instruct-v1 measured on a DGX Spark — Q5_K_M scores 72% on LegalBench (n=50, contains) at 20 tok/s and 4.8 GB. Each card carries perplexity, sustained tok/s, thermal envelope, and a 5-task LegalBench subset score.

uses fieldkit.quantfieldkit.publishfieldkit.evalfieldkit.lineage

Article №37 deployment llama.cpp ~6 hours end-to-end on a DGX Spark
Machine that Builds Machines

Orionfold/finance-chat-GGUF on Spark — five variants, FinanceBench mini-eval, four-axis measurement card

Five GGUF variants of AdaptLLM/finance-chat measured on a DGX Spark — Q8_0 perplexity-matches F16 losslessly, Q4_K_M ships at 31 tok/s. Each card carries perplexity, sustained tok/s, thermal envelope, and FinanceBench accuracy.

uses fieldkit.quantfieldkit.publishfieldkit.evalfieldkit.lineage

Article №27 foundations TensorRT-LLM ~22 minute read
Looking Beyond Spark

Looking Beyond Spark — KV-Cache Arithmetic at Inference

The serving memory bill is not weights. It's KV cache, and KV scales with concurrent users × context length, not parameters. Same four bills as training; different weights. A 70B at 32 users × 16k context wants 168 GB just for KV — and the Spark teaches you the per-token math.

uses fieldkit.capabilities

Article №13 deployment TensorRT-LLM + Triton Inference Server ~4 hours including two container pulls and three engine builds
Second Brain

TensorRT-LLM on the Spark — FP8 Isn't the Reason to Drop NIM. NVFP4 Is.

Dropping below NIM to raw TensorRT-LLM on a GB10 Spark. FP8 beats NIM's vLLM by 10-15% — barely worth the rebuild. NVFP4 beats it by 76% on decode, 43% on TTFT, and ships a 34%-smaller engine. The reason to drop NIM is the Blackwell-native 4-bit kernel, not FP8.