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docs(vault): one fact, one owner — repair the doubled pages, single-source the facts

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Structural repair:
- 13 pages were the same document twice (each exactly 2x its HEAD size),
  a second frontmatter block whose closing fence had been glued onto a body
  line by a frontmatter rewrite that re-read its own output. Un-doubled by
  cutting at the second frontmatter and keeping the copy whose body is a
  superset; the orphan census reports 0 lost tokens against HEAD.
- log.md: one entry per date, bare `## YYYY-MM-DD` headings, newest first —
  66 dated headings merged into 34, titles demoted to bold leads, every
  content line verified present. The file had been descending at the head
  and ascending at the tail.

Ownership moves:
- New `LLM/frank-medium.md` is the locator for the retired Dankserver
  endpoint: era windows quoted from each era page's own banner, no numbers
  restated, and the two unresolved date conflicts recorded instead of
  silently resolved. `frank-archon.md` links to it instead of restating the
  pre-rename window.
- The four Pipeline-Parallelism stubs (frontmatter only, no body) are gone;
  `Multi-Node Runtimes.md` owns runtime construction, launch, mixed-vendor
  verdicts and cross-split spec decode, and the four links that pointed at
  the stubs were repointed at it or at the drafter page.
- Authentik's three stub pages fold into `Authentik.md`, with the process
  detail and the outpost reference split out so the parent stays under the
  length hint.

Verified corrections (host census + live probe, 2026-10-19):
- The MXFP4 checkpoint two pages called the "on-disk rollback" is gone:
  /home/dank/models/ holds StillDeadcode, rad and tpl.jinja. Stated once, on
  the era page that owns that payload; the swap page now links to it.
- frank-medium-radiance-mxfp4: the 4.24/7 accept rate belongs to the DFlash2
  arm measured on the FP8 target, not to the MXFP4 era; the 3-family soak run
  census is kept once (155 requests) instead of twice (~120 and 155).
- Fleet shapes re-confirmed against the live endpoints before touching any
  page: 3x200000 fp8 KV, 2x200704, 2x262144.

Index rows are regenerated, not hand-carved. Net -1,849 vault lines.
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serverAppConfigs-docs/LLM/Pipeline Parallelism/Ethernet Bandwidth Math.md
+21 -97
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@@ -1,11 +1,11 @@
---
type: reference
title: Ethernet Bandwidth Math
description: Bytes-per-token math for pipeline vs tensor parallelism, feasibility tables for 1/2.5/10 GbE, and real multi-node benchmark numbers.
description: "Bytes-per-token math for pipeline versus tensor parallelism, and the link budgets that follow from it."
tags:
- pipeline-parallelism
- research
timestamp: 2026-08-17
timestamp: 2026-10-19
---
# Bandwidth Analysis: Multi-Node LLM Inference over Ethernet
@@ -147,19 +147,11 @@ bytes_per_boundary = batch_size × hidden_size × 2 [fp16 = 2 bytes]
- Per-token, per-boundary: `6144 × 2 = 12,288 bytes = 12 KB`
- With Nₚₚ stage boundaries: total = `Nₚₚ × 12 KB/tok`
**Full example — 27B model, 2 PP stages (1 boundary), batch=1:**
**Worked examples (1 boundary = 2 PP stages):**
```
1 boundary × 4096 × 2 bytes = 8,192 bytes/tok
```
**Example — same model, batch=64:**
```
1 boundary × 4096 × 64 × 2 bytes = 524,288 bytes = 512 KB/tok
```
**Example — 36B model, 2 PP stages, batch=1:**
```
1 boundary × 6144 × 2 bytes = 12,288 bytes = 12 KB/tok
27B, batch=1: 1 boundary × 4096 × 2 bytes = 8,192 bytes/tok = 8 KB
27B, batch=64: 1 boundary × 4096 × 64 × 2 bytes = 524,288 = 512 KB/tok
36B, batch=1: 1 boundary × 6144 × 2 bytes = 12,288 bytes/tok = 12 KB
```
### 1.2 Tensor Parallelism (TP) — for comparison
@@ -244,57 +236,13 @@ total_bytes_per_tok = L × batch_size × hidden_size × 2 [L all-reduces total
| 50 | 10.24 Mbit/s | 163.84 Mbit/s | 655.36 Mbit/s |
| 100 | 20.48 Mbit/s | 327.68 Mbit/s | 1,310.72 Mbit/s |
### 2.3 Feasibility matrix
### 2.3 Feasibility
Green = feasible (< 50% of link capacity). Yellow = marginal (50–90%). Red = saturated (> 90%).
**27B model — PP (1 boundary), batch=1:**
| | 1 GbE (118 MB/s) | 2.5 GbE (294 MB/s) | 10 GbE (1175 MB/s) |
|---|---|---|---|
| 10 tok/s | ✅ 0.13 Mbit/s | ✅ 0.13 Mbit/s | ✅ 0.13 Mbit/s |
| 50 tok/s | ✅ 0.65 Mbit/s | ✅ 0.65 Mbit/s | ✅ 0.65 Mbit/s |
| 100 tok/s | ✅ 1.31 Mbit/s | ✅ 1.31 Mbit/s | ✅ 1.31 Mbit/s |
**27B model — PP (1 boundary), batch=16:**
| | 1 GbE | 2.5 GbE | 10 GbE |
|---|---|---|---|
| 10 tok/s | ✅ 2.05 Mbit/s | ✅ 2.05 Mbit/s | ✅ 2.05 Mbit/s |
| 50 tok/s | ✅ 10.24 Mbit/s | ✅ 10.24 Mbit/s | ✅ 10.24 Mbit/s |
| 100 tok/s | ✅ 20.48 Mbit/s | ✅ 20.48 Mbit/s | ✅ 20.48 Mbit/s |
**27B model — PP (1 boundary), batch=64:**
| | 1 GbE | 2.5 GbE | 10 GbE |
|---|---|---|---|
| 10 tok/s | ✅ 8.19 Mbit/s | ✅ 8.19 Mbit/s | ✅ 8.19 Mbit/s |
| 50 tok/s | ✅ 40.96 Mbit/s | ✅ 40.96 Mbit/s | ✅ 40.96 Mbit/s |
| 100 tok/s | ✅ 81.92 Mbit/s | ✅ 81.92 Mbit/s | ✅ 81.92 Mbit/s |
**27B model — TP (32 layers), batch=1:**
| | 1 GbE | 2.5 GbE | 10 GbE |
|---|---|---|---|
| 10 tok/s | ✅ 2.05 Mbit/s | ✅ 2.05 Mbit/s | ✅ 2.05 Mbit/s |
| 50 tok/s | ✅ 10.24 Mbit/s | ✅ 10.24 Mbit/s | ✅ 10.24 Mbit/s |
| 100 tok/s | ✅ 20.48 Mbit/s | ✅ 20.48 Mbit/s | ✅ 20.48 Mbit/s |
**27B model — TP (32 layers), batch=16:**
| | 1 GbE | 2.5 GbE | 10 GbE |
|---|---|---|---|
| 10 tok/s | ✅ 32.77 Mbit/s | ✅ 32.77 Mbit/s | ✅ 32.77 Mbit/s |
| 50 tok/s | ✅ 163.84 Mbit/s | ✅ 163.84 Mbit/s | ✅ 163.84 Mbit/s |
| 100 tok/s | ✅ 327.68 Mbit/s | ✅ 327.68 Mbit/s | ✅ 327.68 Mbit/s |
**27B model — TP (32 layers), batch=64:**
| | 1 GbE | 2.5 GbE | 10 GbE |
|---|---|---|---|
| 10 tok/s | ✅ 131.07 Mbit/s | ✅ 131.07 Mbit/s | ✅ 131.07 Mbit/s |
| 50 tok/s | ✅ 655.36 Mbit/s | ✅ 655.36 Mbit/s | ✅ 655.36 Mbit/s |
| 100 tok/s | ✅ 1310.72 Mbit/s | ✅ 1310.72 Mbit/s | ✅ 1310.72 Mbit/s |
Thresholds: **feasible** = under 50% of link capacity, **marginal** = 50–90%,
**saturated** = over 90%. Cross-checking §2.2 against §2.1: every PP case is
feasible on every link class — even the heaviest, 27B at batch=64 and 100
tok/s (81.92 Mbit/s), is feasible on 1 GbE — and every TP case is feasible
except batch=64:
**27B TP batch=64 at 100 tok/s: 1310.72 Mbit/s > 1 GbE usable (1000 Mbit/s) — link-saturated on 1 GbE**
@@ -368,10 +316,7 @@ With TP, each layer transfers `H × 2` bytes but this happens L times. For 256 K
**Petals [1]:**
- Uses pipeline parallelism with dynamic 8-bit quantization (halves bandwidth)
- For BLOOM-176B (H=14336), per-boundary communication is ~14 KB/tok with quantization
- Recommended bandwidth: 25 Mbit/s bidirectional for interactive inference [1]
- Key finding: "For inference, performance does not depend much on bandwidth or sequence length but degrades with higher latency" — RTT matters more than throughput
- At 1 Gbit/s with <5 ms RTT: 1.71 steps/s; at 100 Mbit/s with <5 ms: 1.66 steps/s (only 3% difference)
- At 100 Mbit/s with 100 ms RTT: 1.23 steps/s (28% degradation)
**llama.cpp RPC [2]:**
- Model weights and KV cache distributed across nodes proportionally to memory
@@ -493,38 +438,17 @@ With Nₚₚ = 4 and 30 µs per-hop RTT: ~120 µs network, still <6% of compute
## Appendix: Full bandwidth formulae
### Pipeline Parallelism (generation, batch=1)
### Pipeline and Tensor Parallelism (generation)
`N` is the batch size; `N = 1` is the batch=1 form.
```
bytes_per_boundary_per_tok = hidden_size × 2 [fp16]
total_bytes_per_tok = num_boundaries × hidden_size × 2
bytes_per_boundary_per_tok = hidden_size × N × 2 [fp16, PP boundary]
bytes_per_layer_allreduce = hidden_size × N × 2 [TP, one all-reduce per layer]
total_bytes_per_tok = num_boundaries × hidden_size × N × 2 [PP]
total_bytes_per_tok = num_layers × hidden_size × N × 2 [TP]
bandwidth_mbps = total_bytes_per_tok × tokens_per_second × 8 / 1,000,000
```
### Pipeline Parallelism (generation, batch=N)
```
bytes_per_boundary_per_tok = hidden_size × N × 2
total_bytes_per_tok = num_boundaries × hidden_size × N × 2
```
### Tensor Parallelism (generation, batch=1)
```
bytes_per_layer_allreduce = hidden_size × 2
total_bytes_per_tok = num_layers × hidden_size × 2
bandwidth_mbps = total_bytes_per_tok × tokens_per_second × 8 / 1,000,000
```
### Tensor Parallelism (generation, batch=N)
```
total_bytes_per_tok = num_layers × hidden_size × N × 2
```
### With 8-bit quantization (like Petals)
```
# Simply divide all fp16 byte counts by 2
bytes_per_boundary_per_tok = hidden_size × 1 [int8]
# With 8-bit quantization (like Petals): simply divide all fp16 byte counts by 2
bytes_per_boundary_per_tok = hidden_size × 1 [int8]
```