Frank
327e78b950
The vault was 155 pages / 31,210 lines, most of it measurement ledgers for payloads that no longer run. A throughput figure for a runtime nobody operates is not a record: AI moves faster than the vault, so the number is wrong before anyone reads it, and a wrong number in a runbook gets believed. Per the new purpose test in `AGENTS.md` → *Documentation Policy*, it is destroyed. Gone (`.AGENTTRASH/20261019-destruction/`, KILL-LIST.txt is the manifest): - the `frank-medium` endpoint and its five era pages, `frank-npu`, FastFlowLM, the ikllama/kingjones/dealign/frank-redo eras, the ling/swift15/freetoken/ubatch campaign pages, qwen3.8 spec-matrix and strix-optimizations, the two "Running Qwen3.6 …" pages; - the whole `Pipeline Parallelism/` folder (no live script passes a pipeline flag) and the multi-device/x4-pair/hybrid/offload experiment pages; - every vLLM page — no vLLM endpoint exists on this fleet; - `LLM/deployment.md` (`Architecture/Deployment Flow.md` is the test-referenced owner) and the external surveys (SGLang, Lemonade, llama.cpp forks/diff, self-hosted app census, the TTS survey) that were obsolete by construction; - `log.md`, 2,380 lines. Git holds the chronology; a causal law belongs to the page that owns the thing it governs, never to a diary. Survivors: 101 pages, 17,542 lines, 0 OKF violations, 0 unresolved links. Eras tables keep the window, payload, runtime, reason and rollback pointer; the rate columns are blanked. The live shapes stay verbatim — those numbers are what the deployer and `models.yml` consume.
50 lines
3.0 KiB
Markdown
50 lines
3.0 KiB
Markdown
# LLM fleet — index
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The three live endpoint pages are the only pages that carry a shape. Everything
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else here is a technique or a piece of hardware, and each of those owns exactly
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that one thing. There is no worklog and no era page: a payload swap is one row in
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the endpoint's **Eras** table, and the numbers a retired era measured are gone.
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## Live endpoints
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| Page | Owns |
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| [[frank-archon]] | The radiance endpoint: live shape and harness contract, every era with its rollback path, and the laws it taught. |
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| [[frank-dragoon]] | The iGPU endpoint: live shape, every era with its rollback path, the laws it paid for, and the files that own it. |
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| [[frank-zealot]] | The RTX 3090 endpoint: live shape exactly as the env ships it, every era with its rollback path, and its card laws. |
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## Retired endpoints
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| Page | Owns |
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## Pipeline Parallelism (series)
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| Page | Owns |
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## Techniques, hardware, trials
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| Page | Owns |
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| [[Dankclaw Improvements]] | This page collects concrete, sourced recommendations to improve Dankclaw |
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| [[Dankclaw System Specs]] | Dankclaw is an AMD APU server for CIRU ROCmFPX llama.cpp inference. |
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| [[dankclaw-iommu-translation-off]] | Why Dankclaw boots with identity DMA, what the earlier off-era bought and cost, and which tooling owns the boot line. |
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| [[Dankserver System Specs]] | Dankserver hardware and OS state since the board swap: board, lane topology, cards, memory, kernel posture. |
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| [[DFlash DSpark Speculative Decoding]] | DFlash and DSpark are the two newest speculative decoding methods. |
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| [[GGUF Compatibility]] | Answering whether an engine loads a GGUF from the file header alone, and pinning a fork build that can. |
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| [[Heterogeneous Split Triage]] | Triage before splitting across unequal accelerators: link characterisation, real bottleneck, what placement never buys. |
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| [[Llama Cpp Serving Sizing]] | Measurement-first sizing for llama.cpp shapes: slot pools against a headroom band, per-device accounting, prompt cache. |
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| [[LLM Performance Diagnosis]] | Probing why a served model is slow: the flat-depth probe, offload duty-cycle arithmetic, and profiler attribution. |
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| [[Model Swap and Rollback]] | The model-swap discipline: fit preflight, fetch verification, build and flag checks, live validation, three-pin revert. |
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| [[PCIe Link and Atomics]] | Link probing, the streaming-versus-compute ceiling arithmetic, and the atomics wall that decides multi-GPU collectives. |
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| [[Quantization by Hardware Architecture]] | This page maps LLM quantization formats to the GPU and CPU architectures that support them. |
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| [[Quantization Production]] | Making a quantized payload here: the conversion recipe, the scoring gate, and the campaign ladder behind a shipped file. |
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| [[RDNA4 MoE Expert Offload VRAM Budget]] | Expert-offload arithmetic on the RDNA4 fork: movable terms, the utilisation law, graph charge, ceilings, context ladder. |
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| [[Shared Memory Architecture Dankclaw]] | Dankclaw uses AMD Strix Halo APU with unified memory architecture. |
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## Pages
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| Page | Owns |
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