docs: add Dankclaw improvements research (agentic tools, runtime tuning, server/OS)

2026-08-11 18:14:44 -07:00
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+# Dankclaw — Improvements Research
+
+This page collects concrete, sourced recommendations to improve Dankclaw
+(the AMD Strix Halo inference box). It covers self-hosted agentic tools,
+inference runtime tuning, and server/OS/hardware settings. Each item lists a
+priority and the exact action.
+
+Dates: researched 2026-08. Hardware is verified on the live machine unless
+marked "verify first".
+
+## Verified Memory Picture (measured 2026-08-11)
+
+| Figure | Value | Source |
+| ------ | ----- | ------ |
+| OS physical RAM | 30.5 GiB | `/proc/meminfo` |
+| TTM pages_limit | 15.24 GiB (3,995,281 x 4KiB) | `/sys/module/ttm/parameters/pages_limit` |
+| ROCm VRAM total | 96.0 GiB | `rocm-smi --showmeminfo vram` |
+| ROCm VRAM used | 76.2 GiB | `rocm-smi` while serving 3x200700 |
+
+The OS accounts only ~30.5 GiB physical RAM, and the TTM GPU-addressable pool
+is capped at ~15.24 GiB. ROCm/SMI still reports a 96 GiB VRAM carve-out (VGM).
+This split is the Strix Halo GTT/VGM design. It drives the batch/ubatch memory
+limits documented in [[frank-default-large-benchmark]].
+
+## 1. Self-Hosted Agentic Tools
+
+All of these connect to the local llama.cpp OpenAI-compatible endpoint
+(`http://localhost:7770/v1`). None need NVIDIA.
+
+| Tool | Use | Endpoint config | Priority |
+| ---- | --- | --------------- | -------- |
+| Open WebUI | Chat + agent harness, plugins, RAG, tool calling | Add OpenAI-compatible provider -> `http://localhost:7770/v1` | High |
+| Cline (VS Code) | IDE coding agent with MCP | Provider "OpenAI Compatible", `API_URL=http://localhost:7770/v1` | High |
+| Aider (CLI) | Terminal pair-programming agent | `OPENAI_API_BASE=http://localhost:7770/v1` | Medium |
+| Continue.dev (VS Code) | Open-source coding agent | `config.json` -> `{type:"openai", apiBase:"http://localhost:7770"}` | Medium |
+| OpenHands (Agent Canvas) | Self-hosted multi-agent coding platform | LiteLLM custom model + Base URL to the endpoint | Medium |
+| AnythingLLM | RAG / knowledge chat | Provider "OpenAI (Generic)" -> endpoint | Medium |
+| Browser Use | Browser automation agent (Playwright) | `ChatOpenAI(base_url="http://localhost:7770/v1")` | Low |
+| Qdrant | Vector store for RAG | Standalone, CPU-native | Low |
+
+The pragmatic first installs are Open WebUI, Cline, and Aider. They all work
+with the current llama.cpp server and need no extra GPU.
+
+## 2. Inference Runtime Tuning (llama.cpp on gfx1151)
+
+Verified against the running service and llama.cpp source.
+
+| Change | Recommendation | Priority |
+| ------ | -------------- | -------- |
+| Backend | Keep HIP/ROCm. HIP out-performs Vulkan for decode on RDNA3.5/4 iGPU. | Done |
+| KV cache | Keep `q8_0` K / `f16` V. q8_0 halves memory vs f16 with negligible loss. Try `q4_0`/`iq4_nl` only if more context is needed. | Keep |
+| Flash attention | Leave `--flash-attn auto`. It helps prompt processing, not decode. | Keep |
+| Load mode | Start with mmap (default). Enable `--mlock` only if page-fault stutter appears. | Optional |
+| Threads | Use `--threads 12 --threads-batch 16 --threads-http 1` for 16 Zen5 cores + 3 slots. Frees cores for the ROCm driver. | Medium |
+| DFlash n_max | Current 8 (block 16) is high. Try `--spec-draft-n-max 5`; it may reduce rejection cost at the same throughput. | Verify |
+| ngram layering | Measured slower than DFlash alone at 4k (16.9 vs 20.2 t/s). Not enabled. | Keep off |
+
+## 3. Server / OS / Hardware Settings
+
+All require root and a reboot for the modprobe change.
+
+| Setting | Action | Effect | Priority |
+| ------- | ------ | ------ | -------- |
+| TTM GPU pool | Set `amd-ttm --set 96` (write `/etc/modprobe.d/ttm.conf`), reboot | Lifts the 15 GiB TTM cap toward full RAM; more headroom for GPU buffers | High |
+| Power profile | `echo performance > /sys/class/drm/card0/device/power_profile` | Sustained inference speed; apply at boot via a udev/systemd unit | Medium |
+| NVMe scheduler | Already `none` (`[none]` confirmed) | No change | Done |
+| BIOS VGM | Set Variable Graphics Memory / UMA to "High" in the EVO-X2 BIOS | Max dedicated VRAM for the GPU carve-out; verify in the live BIOS | Verify |
+| Firmware/EC | Check GMKtec for a newer EC/fan-curve firmware | Better sustained thermal behavior | Verify |
+
+## 4. Other Enhancements
+
+- Test `--spec-draft-n-max 5` and re-benchmark acceptance; if throughput rises,
+  change the env tunable.
+- Consider switching the disabled `vllm-thinkingcap.service` unit off or
+  removing it to avoid confusion (it is loaded in a failed state).
+- Profile generation under 3-slot concurrent load before touching boot
+  settings, so any tuning is measured against the real workload.
+
+## Sources
+
+- [[frank-default-large-benchmark]] — measured backend/spec numbers
+- [[deployment]] — deploy and update process
+- AMD ROCm RDNA3.5 system optimization guide (gfx1151 memory/BIOS)
+- llama.cpp source: `ggml/CMakeLists.txt`, `ggml/src/ggml-hip/CMakeLists.txt`,
+  `common/arg.cpp`, `common/common.h`
+- NotebookCheck Ryzen AI MAX+ 395 analysis
+
+## See Also
+
+- [[frank-default-large-benchmark]]
+- [[DFlash DSpark Speculative Decoding]]
+- [[deployment]]
+- [[index]]
@@ -52,6 +52,7 @@ DFlash draft, port 7770]
 - [[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
 - [[frank-default-large-benchmark]] — backend/spec benchmark that drove the Dankclaw llama.cpp deployment
+- [[Dankclaw Improvements]] — researched enhancements: self-hosted agentic tools, runtime tuning, server/OS settings

 ## Speculative Decoding & Quantization