docs: pipeline parallelism research + educational sample script set

Compiles the 5-subagent research fan-out (llama.cpp RPC, vLLM PP,
bandwidth math, mixed-vendor experiments, Qwen3.6-27B spec methods) into
the KB under docs/Pipeline Parallelism/ with an index, expected
performance estimates (1/2.5/10 GbE), and an EDUCATIONAL-ONLY two-node
Qwen3.6-27B Q8 split script set (MTP/DFlash/ngram-mod variants, DSpark
noted as vLLM-only). Nothing here is deployed or intended to run.

docs/Pipeline Parallelism/index.md

@@ -0,0 +1,73 @@
+---
+type: index
+title: Pipeline Parallelism
+description: Multi-node LLM inference research and sample scripts for llama.cpp RPC and vLLM PP over ethernet.
+tags:
+  - pipeline-parallelism
+  - distributed
+  - index
+timestamp: 2026-08-14
+---
+
+# Pipeline Parallelism
+
+This section covers running one large model across two machines. It focuses
+on pipeline parallelism (layer split) over ethernet. The research answers
+what speeds are possible and which tools work. The sample scripts show a
+two-node Qwen3.6-27B Q8 split for reading only.
+
+## Terminology
+
+Pipeline parallelism (PP) splits model layers across machines. Each machine
+computes its layers and passes hidden states to the next machine. Tensor
+parallelism (TP) splits each layer across machines and requires an
+all-reduce per layer. PP is the only realistic option over ethernet.
+
+Speculative decoding is a separate feature. It uses a small draft model (or
+n-grams) to propose tokens that the main model verifies in one pass. It can
+run together with PP in llama.cpp.
+
+## The Two Runtimes
+
+```mermaid
+graph LR
+    subgraph llamaRPC[llama.cpp RPC]
+        A1[ggml-rpc-server on node B] --> A2[llama-server on node A<br/>--rpc nodeB:50052]
+    end
+    subgraph vllmPP[vLLM PP]
+        B1[vllm serve node 0<br/>tp=1 pp=2 rank 0] <-->|hidden states| B2[vllm serve node 1<br/>tp=1 pp=2 rank 1]
+    end
+```
+
+- [[llama.cpp RPC]] — the working, production-capable path for two
+  heterogeneous nodes over ethernet.
+- [[vLLM Pipeline Parallelism]] — supported but heavier; needs Ray or
+  multiprocessing and does not compose with speculative decoding yet.
+
+## What the Research Found
+
+- [[Ethernet Bandwidth Math]] — a PP boundary moves only the hidden state
+  (about 10 KiB per token for a 27B model). 1 GbE supports hundreds of
+  tokens per second on paper; the real limit is latency and RPC overhead.
+- [[Mixed-Vendor Experiments]] — NVIDIA + Strix Halo multi-node inference
+  works today via llama.cpp RPC with separate CUDA and ROCm builds. The
+  exact NVIDIA-desktop + Strix-Halo-NUC pair has not been benchmarked with
+  published numbers yet.
+- [[Qwen3.6 27B Spec Methods]] — Q8 GGUF variants, and which speculative
+  methods (MTP, DFlash, ngram-mod) work with RPC. DSpark is vLLM-only.
+- [[Expected Performance]] — estimated tokens per second for a 27B Q8 split
+  across an RTX 3090 and a Strix Halo NUC over 1/2.5/10 GbE.
+
+## Sample Scripts
+
+- [[Sample Script Set]] — a two-node llama.cpp RPC split of
+  Qwen3.6-27B-Q8_K_P with MTP, DFlash, and ngram-mod variants, sized for
+  the largest context that fits. For educational reading only. Do not run
+  it in production.
+
+## See Also
+
+- [[DFlash DSpark Speculative Decoding]]
+- [[frank-default-small-benchmark]]
+- [[Dankserver System Specs]]
+- [[Dankclaw System Specs]]