diff --git a/docs/DFlash DSpark Speculative Decoding.md b/docs/DFlash DSpark Speculative Decoding.md
new file mode 100644
index 0000000..940fe09
--- /dev/null
+++ b/docs/DFlash DSpark Speculative Decoding.md	
@@ -0,0 +1,181 @@
+# DFlash and DSpark Speculative Decoding
+
+## Overview
+
+DFlash and DSpark are the two newest speculative decoding methods. They draft many tokens in one parallel pass instead of one token at a time. This reduces decode latency on memory-bound workloads.
+
+DFlash comes from Z Lab at UC Berkeley. DSpark comes from DeepSeek. Both work with vLLM and llama.cpp. Both have draft models for the Qwen3.6 models that Dankserver and Dankclaw run.
+
+## How DFlash Works
+
+DFlash is a lightweight block diffusion model. It predicts a block of tokens in a single forward pass. Key design points:
+
+- The draft model reuses the embedding and LM head from the target model.
+- Only the intermediate layers are trained.
+- It fuses target-model hidden features into the KV projections of every draft layer.
+- A 16-token DFlash drafter has lower latency than a 1-layer EAGLE-3 drafter producing 8 tokens.
+
+Results from the DFlash paper (arXiv:2602.06036):
+
+- Over 6x lossless speedup on Qwen3-8B.
+- Up to 2.5x higher speedup than EAGLE-3.
+- Diffusion drafting cost is flat, so block size does not increase cost.
+
+## How DSpark Works
+
+DSpark is a semi-autoregressive extension of DFlash from DeepSeek (arXiv:2607.05147). It keeps the DFlash parallel diffusion backbone and adds a lightweight sequential Markov head. This models inter-token dependencies inside each block. It also uses confidence-scheduled verification, which stops verifying tokens that have a high rejection risk.
+
+Results from the DSpark paper:
+
+- 60-85% faster per-user generation than MTP-1 on DeepSeek-V4 production traffic.
+- Useful when parallel drafts decay in acceptance rate.
+
+Both DFlash and DSpark are lossless under rejection sampling. Use the strict rejection sampler.
+
+## Runtime Support
+
+| Runtime | DFlash | DSpark | MTP |
+|---------|--------|--------|-----|
+| vLLM | yes (v0.20.1+) | yes | yes |
+| llama.cpp | yes | yes | yes |
+| SGLang | yes | no | yes |
+
+### vLLM
+
+vLLM v0.20.1+ has core DFlash support. Use the `--speculative-config` JSON object.
+
+DFlash on vLLM:
+
+```
+vllm serve Qwen/Qwen3.6-35B-A3B \
+    --speculative-config '{"method": "dflash", "model": "z-lab/Qwen3.6-35B-A3B-DFlash", "num_speculative_tokens": 15}' \
+    --attention-backend flash_attn \
+    --max-num-batched-tokens 32768
+```
+
+Legacy flags also work:
+
+```
+vllm serve Qwen/Qwen3.6-35B-A3B \
+    --spec-method dflash \
+    --spec-model z-lab/Qwen3.6-35B-A3B-DFlash \
+    --spec-tokens 8
+```
+
+DFlash requires a non-causal attention backend. Set `"attention_backend": "flash_attn"` in the speculative config if needed. On ROCm, check that the flash_attn backend is available before using DFlash.
+
+DSpark on vLLM:
+
+```
+vllm serve <target-model> \
+    --speculative-config '{"method": "dspark", "model": "<dspark-draft>", "num_speculative_tokens": 15}'
+```
+
+### llama.cpp
+
+llama.cpp supports DFlash, DSpark, and MTP through `--spec-type`. When you pass `--spec-draft-hf`, llama.cpp auto-discovers the sidecar files and infers the type. The priority order is DSpark, DFlash, EAGLE-3, MTP.
+
+DFlash on llama.cpp:
+
+```
+llama-server --hf-repo <target-GGUF> \
+    --spec-draft-hf <draft-GGUF> \
+    --spec-type draft-dflash \
+    --spec-draft-n-max 16
+```
+
+DSpark on llama.cpp:
+
+```
+llama-server --hf-repo <target-GGUF> \
+    --spec-draft-hf <draft-GGUF> \
+    --spec-type draft-dspark
+```
+
+MTP on llama.cpp:
+
+```
+llama-server --hf-repo <target-with-MTP> \
+    --spec-type draft-mtp \
+    --spec-draft-n-max 2
+```
+
+## Draft Models
+
+DFlash draft model sizes for the models on these servers:
+
+| Target model | DFlash draft | Draft size |
+|--------------|--------------|------------|
+| Qwen3.6-35B-A3B | z-lab/Qwen3.6-35B-A3B-DFlash | 0.4B |
+| Qwen3.6-27B | z-lab/Qwen3.6-27B-DFlash | 2B |
+| Qwen3.5-35B-A3B | z-lab/Qwen3.5-35B-A3B-DFlash | 0.4B |
+| Qwen3.5-27B | z-lab/Qwen3.5-27B-DFlash | 2B |
+
+The DFlash draft is not a standalone model. It must pair with its target model. The model cards recommend block size 8 for higher concurrency and 16 for single-stream throughput.
+
+## Deployment on Dankserver (NVIDIA RTX 3090)
+
+Dankserver runs vLLM in a Podman container with the RTX 3090. The RTX 3090 is Ampere (compute capability 8.6). Use these conditions:
+
+- FP8 KV cache is valid on CUDA 12.
+- FP8 weights compute through a fallback path; no native FP8 tensor cores.
+- The DFlash draft model (0.4B) fits easily in 24 GB VRAM alongside the target.
+
+Working DFlash flags for this server:
+
+```
+--spec-method dflash
+--spec-model z-lab/Qwen3.6-35B-A3B-DFlash
+--spec-tokens 8
+--kv-cache-dtype fp8
+```
+
+Example DFlash + AWQ on the RTX 3090:
+
+```
+docker run --rm --name vllm-qwen36-dflash \
+    --gpus all --ipc=host \
+    -p 8000:8000 \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    docker.io/vllm/vllm-openai:latest \
+    cyankiwi/Qwen3.6-35B-A3B-AWQ-4bit \
+    --speculative-config '{"method": "dflash", "model": "z-lab/Qwen3.6-35B-A3B-DFlash", "num_speculative_tokens": 8}' \
+    --attention-backend flash_attn \
+    --gpu-memory-utilization 0.95
+```
+
+## Deployment on Dankclaw (AMD Strix Halo gfx1151)
+
+Dankclaw runs the ROCmFPX llama.cpp runtime as its primary service. The active service uses the Ling-3.0-flash ROCmFP4 MTP model with `--spec-type draft-mtp`. This gives measured results:
+
+- 45-50 t/s decode with MTP speculation.
+- ~37 t/s plain decode.
+- ~95% draft acceptance with `--spec-draft-n-max 2`.
+
+ROCmFPX is a llama.cpp fork by charlie12345. It adds ROCmFP4 and ROCmFP2 GGUF weight formats with HIP/ROCm and Vulkan kernels. It is not merged into upstream llama.cpp.
+
+Ling-3.0-flash-related facts:
+
+- Base model: inclusionAI/Ling-3.0-flash, 124B total / 5.1B active.
+- The MTP (NextN) head is preserved in the GGUF conversion.
+- The raulvidis GGUF repo targets Strix Halo (gfx1151).
+
+If you want DFlash on Dankclaw, use a DFlash GGUF draft. Community conversions exist, for example Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp. The ROCm path for DFlash is newer than the MTP path and less proven on gfx1151.
+
+## Notes
+
+- Speculative decoding helps low-QPS, memory-bound, latency-focused workloads. It helps less at high QPS.
+- DFlash needs a non-causal attention backend; the ROCm flash_attn backend must be validated before production use.
+- The old `--swap-space` flag no longer exists in current vLLM. Use `--kv-offloading-size`.
+- `--speculative-config` is the preferred CLI. Legacy `--spec-method`, `--spec-model`, and `--spec-tokens` flags still exist.
+- Use `rejection_sample_method` default `strict` for lossless output.
+
+## See Also
+
+- [[Quantization by Hardware Architecture]]
+- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]
+- [[Running Qwen3.6 27B FP8 on Dankclaw]]
+- [[Dankclaw System Specs]]
+- [[Dankserver System Specs]]
+- [[VLLM Overview]]
+- [[deployment]]
\ No newline at end of file
diff --git a/docs/Quantization by Hardware Architecture.md b/docs/Quantization by Hardware Architecture.md
new file mode 100644
index 0000000..7122a65
--- /dev/null
+++ b/docs/Quantization by Hardware Architecture.md	
@@ -0,0 +1,131 @@
+# Quantization by Hardware Architecture
+
+## Overview
+
+This page maps LLM quantization formats to the GPU and CPU architectures that support them. It answers one question: which quantization type works well on a given piece of hardware?
+
+Use this page when you choose a model format for Dankserver or Dankclaw. The short answer:
+
+- NVIDIA Ampere (RTX 30 series): AWQ or GPTQ 4-bit.
+- NVIDIA Ada (RTX 40 series): FP8 or AWQ.
+- NVIDIA Hopper / Blackwell (H100, RTX 50 series): FP8, NVFP4.
+- AMD CDNA (MI300 series): FP8 or MXFP4.
+- AMD RDNA 3 / Strix Halo (gfx11): GGUF Q4_K_M or ROCmFP4.
+- CPU-only: any GGUF quant.
+
+## Quantization Types
+
+### FP8 (E4M3)
+
+FP8 uses 8 bits per weight. vLLM supports two modes: W8A8 (quantize weights and activations) and W8A16 (weights only).
+
+| Hardware | Native support | Notes |
+|----------|----------------|-------|
+| NVIDIA Hopper (SM 9.0) | yes | full FP8 tensor cores |
+| NVIDIA Ada (SM 8.9) | yes | full FP8 tensor cores |
+| NVIDIA Ampere (SM 8.0/8.6) | no | W8A16 via Marlin; W8A8 runs via fallback |
+| NVIDIA Turing (SM 7.5) | no | W8A16 via Marlin only |
+| NVIDIA Blackwell (SM 12.0) | yes | FP8 and native FP4 |
+| AMD MI300/MI350 (gfx942/gfx950) | yes | via ROCm AITER |
+| AMD MI250/MI325 | no | simulation: dequantize to FP16 |
+| AMD RDNA 3/3.5 (gfx11/gfx1151) | no | software emulation only |
+| CPU | no | FP8 models run via dequant |
+
+Rules:
+
+- Use FP8 on Ada, Hopper, Blackwell, or MI300+.
+- On Ampere (RTX 3090), FP8 gives memory savings only. Run AWQ or BF16 instead.
+- Do not expect compute speedup from FP8 on RDNA or on the RTX 3090.
+
+### INT8
+
+INT8 W8A8 is broadly supported.
+
+| Hardware | Native support |
+|----------|----------------|
+| NVIDIA Volta through Blackwell | yes |
+| AMD MI300/MI350 | yes |
+| AMD RDNA 3/3.5 | no |
+| x86 CPU | yes, via llama.cpp or llm-compressor |
+
+### INT4 (AWQ, GPTQ)
+
+INT4 is the standard choice for 24 GB GPUs such as the RTX 3090.
+
+| Hardware | AWQ | GPTQ |
+|----------|-----|------|
+| NVIDIA Turing | yes | yes |
+| NVIDIA Ampere | yes | yes |
+| NVIDIA Ada / Hopper / Blackwell | yes | yes |
+| AMD ROCm (vLLM) | no | no |
+| AMD ROCm (Quark) | yes | yes |
+| AMD ROCm GGUF | yes | yes |
+
+Rules:
+
+- AWQ is the default choice for NVIDIA with vLLM.
+- On AMD, use GGUF INT4 or the AMD Quark toolkit. vLLM does not list AWQ or GPTQ in its native ROCm matrix.
+
+### GGUF (Q4_0, Q5_K_M, Q8_0, etc.)
+
+GGUF runs on CPU everywhere. GPU acceleration is available through CUDA, ROCm, SYCL, and Vulkan backends. This is the format llama.cpp uses.
+
+Good GGUF quant choices:
+
+| Use case | Quant |
+|----------|-------|
+| Quality first | Q8_0, Q6_K |
+| Balanced | Q5_K_M, Q4_K_M |
+| Small footprint | Q4_0, IQ4_XS |
+| Smallest | IQ2_XS, Q2_K |
+
+### FP4
+
+FP4 has two variants: NVFP4 (NVIDIA) and MXFP4 (OCP standard).
+
+| Hardware | NVFP4 | MXFP4 |
+|----------|-------|-------|
+| NVIDIA Blackwell | native | aligned |
+| NVIDIA Ada / Ampere | emulation | emulation |
+| AMD MI300X/MI355 | via ROCm | native |
+| AMD MI250/MI325 | simulation | simulation |
+| AMD RDNA 3 | emulation | aligned, beta |
+
+### ROCmFP4 (ROCmFPX fork)
+
+ROCmFP4 is a GGUF weight format from the ROCmFPX llama.cpp fork. It targets AMD hardware, especially Strix Halo (gfx1151). It is roughly 4.25 bpw and about 12% smaller than Q4_K_M. It uses HIP/ROCm and Vulkan kernels.
+
+Use ROCmFP4 only with the ROCmFPX fork of llama.cpp. Upstream llama.cpp cannot read these files.
+
+## Choices for This Project
+
+### Dankserver (RTX 3090, 24 GB, Ampere)
+
+The RTX 3090 has no native FP8 or FP4 tensor cores. Use:
+
+| Ranking | Format | Why |
+|---------|--------|-----|
+| 1 | AWQ 4-bit | best fit for 24 GB; native kernels |
+| 2 | GPTQ 4-bit | good alternative to AWQ |
+| 3 | BF16 + KV offload | quality reference; CPU-offload experts |
+| 4 | FP8 | only if the FP8 file is all you have; memory savings, no compute win |
+
+### Dankclaw (Strix Halo gfx1151, RDNA 3.5)
+
+RDNA 3.5 has no native FP8 tensor cores. The APU shares memory. Use GGUF through llama.cpp, or ROCmFPX formats:
+
+| Ranking | Format | Why |
+|---------|--------|-----|
+| 1 | ROCmFP4 (ROCmFPX fork) | native ROCm kernels; proven with Ling-3.0-flash MTP |
+| 2 | GGUF Q4_K_M / Q8_0 | works with upstream llama.cpp |
+| 3 | FP8 (vLLM) | software emulation; memory savings only |
+
+## See Also
+
+- [[DFlash DSpark Speculative Decoding]]
+- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]
+- [[Running Qwen3.6 27B FP8 on Dankclaw]]
+- [[Dankclaw System Specs]]
+- [[Dankserver System Specs]]
+- [[VLLM Overview]]
+- [[Shared Memory Architecture Dankclaw]]
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
index 8df746d..356fc78 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -47,3 +47,8 @@ graph LR
 
 - [[Running Qwen3.6 35B A3B FP8 on Dankserver]] — MoE model (35B total, 3B active) on RTX 3090
 - [[Running Qwen3.6 27B FP8 on Dankclaw]] — Dense model (27B) on Strix Halo APU
+
+## Speculative Decoding & Quantization
+
+- [[DFlash DSpark Speculative Decoding]] — block diffusion (DFlash) and semi-autoregressive (DSpark) drafting on vLLM and llama.cpp
+- [[Quantization by Hardware Architecture]] — which quant format fits which GPU/CPU