Frank 993b6af576 docs: add DFlash/DSpark speculative decoding research + quantization-by-hardware guide
- DFlash DSpark Speculative Decoding.md: verified method details (DFlash
  block diffusion from Z Lab arXiv:2602.06036; DSpark semi-autoregressive from
  DeepSeek arXiv:2607.05147), runtime support matrix (vLLM/llama.cpp/SGLang),
  exact vLLM --speculative-config recipes (incl. dflash/dspark), llama.cpp
  --spec-type recipes with sidecar auto-discovery, draft model table
  (z-lab Qwen3.6-27B-DFlash 2B, Qwen3.6-35B-A3B-DFlash 0.4B), deployment
  guidance for RTX 3090 (AWQ+Dflash) and Strix Halo (ROCmFPX MTP), measured
  45-50 t/s MTP decode on Strix Halo
- Quantization by Hardware Architecture.md: FP8/INT8/INT4/GPTQ/AWQ/GGUF/FP4/
  ROCmFP4 support per NVIDIA (Turing/Ampere/Ada/Hopper/Blackwell), AMD
  (CDNA MI250/MI300/MI350, RDNA 3/3.5/4), and CPU; recommended picks per
  Dankserver (AWQ/GPTQ 4-bit) and Dankclaw (ROCmFP4/Q4_K_M)
- index.md: link both new pages under Speculative Decoding & Quantization
2026-08-10 21:09:17 -07:00

vLLM Scripts

Scripts for running large language models via vLLM on two local machines.

Targets

Host User Codename Architecture
dankserver dank DankServer NVIDIA GPU + System RAM
dankclaw dank DankClaw AMD Strix Halo (Shared Memory)

Models

Model Target Format
Qwen3.6 35B A3B FP8 dankserver FP8
Qwen3.6 27B FP8 dankclaw FP8

Structure

  • docs/ — Project knowledge base with research notes on vLLM configuration, hardware specifics, and deployment notes
  • scripts/ — Executable vLLM launch scripts (coming soon)

Knowledge Base

See docs/README.md for the project knowledge base index.

S
Description
Podman deployment configs for Dankserver and Dankclaw
Readme
8.2 MiB
Languages
Shell 60.4%
Python 21%
HTML 6.2%
Jinja 3.9%
C++ 3.9%
Other 4.6%