Frank
90c4ac69a7
Fanned out 5 read-only doc-auditor subagents (one per service group) that fact-checked the knowledge base against the live system + repo config, researched official setup docs online, and verified progress. Applied: - All 34 broken links fixed (real pages / external URLs / correct relative paths); 0 broken links remain. No .AGENTTRASH links (the 4 mentions are accurate descriptive text, not links). - Fact-check fixes: Readarr retired (May 2026), Radarr v4->v6, Calibre 9.2.1 live, Calibre-Web 0.6.25 live, Copyparty v1.20.21, LLM index ncmoe 38 + 786432 ctx, frank-default-small active (not frank-pp-worker), Hermes/SearXNG/Grafana hostnames, Loki/Alloy local images, Observability diagram (Promtail->Alloy), VLLM retired note, DFlash status, benchmark live-drift notes, Homepage test count 116.
96 lines
3.1 KiB
Markdown
96 lines
3.1 KiB
Markdown
# vLLM Index
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> **RETIRED (2026-08-18).** The vLLM service on Dankclaw was replaced by the CIRU ROCmFPX llama.cpp runtime (v3). These pages are historical reference. See [[ciru-activefpx-promptforge]].
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## Documentation Overview
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This index contains all documentation for vLLM deployment on Dankserver and Dankclaw.
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## Hardware Guides
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- [[Dankserver System Specs]] — NVIDIA GPU server specifications
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- [[Dankclaw System Specs]] — AMD GPU server specifications
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## vLLM Architecture
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- [[VLLM Overview]] — Complete vLLM documentation including installation, CLI, API, and key arguments
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- [[RAM CPU Offloading in VLLM]] — CPU RAM offloading mechanisms, swap space, and memory configuration
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- [[Hybrid CPU GPU Processing Dankserver]] — NVIDIA GPU with system RAM hybrid processing
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- [[Shared Memory Architecture Dankclaw]] — AMD ROCm unified memory and APU configuration
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## Model Deployment Guides
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- [[Running Qwen3.6 35B A3B FP8 on Dankserver]] — Qwen3.6-35B-A3B FP8 deployment on NVIDIA GPU
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- [[Running Qwen3.6 27B FP8 on Dankclaw]] — Qwen3.6-27B FP8 deployment on AMD GPU
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## Quick Reference
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### Installation Commands
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```
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# NVIDIA GPU
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uv pip install vllm --torch-backend=auto
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# AMD ROCm
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uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/
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# CPU
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uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly/cpu/
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```
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### Server Start Commands
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```
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# Basic server
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vllm serve Qwen/Qwen2.5-1.5B-Instruct
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# With tensor parallelism
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vllm serve meta-llama/Llama-3.1-70B-Instruct --tensor-parallel-size 4
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# With quantization
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vllm serve Qwen/Qwen2.5-1.5B-Instruct --quantization fp8
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# With GPU memory control
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vllm serve Qwen/Qwen2.5-1.5B-Instruct --gpu-memory-utilization 0.8 --swap-space 8
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```
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### Common API Endpoints
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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| /v1/models | GET | List available models |
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| /v1/completions | POST | Text completion |
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| /v1/chat/completions | POST | Chat completion |
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| /health | GET | Health check |
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| /metrics | GET | Prometheus metrics |
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### Key Parameters
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| Parameter | Description | Typical Value |
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|-----------|-------------|---------------|
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| tensor_parallel_size | Number of GPUs for model sharding | 1-8 |
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| gpu_memory_utilization | Fraction of GPU memory for KV cache (default 0.92) | 0.7-0.95 |
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| swap_space | Removed in current vLLM; use `--kv-offloading-size` | n/a |
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| max_model_len | Maximum context length | auto |
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| max_num_batched_tokens | Max tokens per batch | 2048-8192 |
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| max_num_seqs | Max sequences per batch | 128-512 |
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### Quantization Methods
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| Method | GPU Support | Description |
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|--------|-------------|-------------|
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| FP8 | Hopper, Ada (native); Ampere W8A16 via Marlin; RDNA via emulation | FP8 E4M3 inference |
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| INT8 | Most GPUs | Weight-only INT8 |
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| GPTQ | Most GPUs (NVIDIA; limited on ROCm) | Post-training GPTQ |
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| AWQ | Turing+ (NVIDIA) | Activation-aware weight quantization |
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| GGUF | CPU, GPU | General GPU unification format |
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## See Also
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- [[VLLM Overview]]
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- [[RAM CPU Offloading in VLLM]]
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- [[Hybrid CPU GPU Processing Dankserver]]
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- [[Shared Memory Architecture Dankclaw]]
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- [[Running Qwen3.6 35B A3B FP8 on Dankserver]]
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- [[Running Qwen3.6 27B FP8 on Dankclaw]]
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