Frank
3a64c8f3dd
Correct stale and incorrect facts after exhaustive verification: - Dankclaw: NUMA node 0 = 31213 MB; disk 661G/1.1T/38%; vLLM 0.26.0 in /home/dank/vllm-rocm-venv (not vllm-env); clarify 96 GiB GPU-visible is a driver overcommit ceiling over 30 GiB physical (confirmed via sysfs mem_info_vram_total=103079215104) - Dankserver: disk 729G/140G free/84%; /mnt/entropy 910G free; active service is vLLM EngineCore (22 GB), not llama-server; CPU model Ryzen 7 7700X - index.md: Dankclaw 30GiB phys / 96GiB GPU-visible (was '61 GB unified'); add deployment topology mermaid diagram - vLLM defaults (from source): gpu_memory_utilization=0.92, max_num_seqs=128, swap_space removed (now --kv-offloading-size) across Overview/Index/RAM/Hybrid - Quantization: FP8 not native on Ampere (RTX 3090) or RDNA 3.5 (gfx1151); add per-model hardware quantization notes; AWQ/GPTQ limited on ROCm - Speculative decoding: add DFlash, DSpark, MTP, EAGLE3 to feature list - Confirm Qwen3.6-27B and Qwen3.6-35B-A3B specs are correct (HF-verified) - Production note: Dankclaw primary llama.cpp service runs ROCmFPX Ling-3.0-flash RocmFP4 MTP, not vLLM Qwen3.6-27B
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VLLM Index
Documentation Overview
This index contains all documentation for VLLM deployment on Dankserver and Dankclaw.
Hardware Guides
- Dankserver System Specs -- NVIDIA GPU server specifications
- Dankclaw System Specs -- AMD GPU server specifications
VLLM Architecture
- VLLM Overview -- Complete VLLM documentation including installation, CLI, API, and key arguments
- RAM CPU Offloading in VLLM -- CPU RAM offloading mechanisms, swap space, and memory configuration
- Hybrid CPU GPU Processing Dankserver -- NVIDIA GPU with system RAM hybrid processing
- Shared Memory Architecture Dankclaw -- AMD ROCm unified memory and APU configuration
Model Deployment Guides
- Running Qwen3.6 35B A3B FP8 on Dankserver -- Qwen3-30B-A3B FP8 deployment on NVIDIA GPU
- Running Qwen3.6 27B FP8 on Dankclaw -- Qwen3-32B FP8 deployment on AMD GPU
Quick Reference
Installation Commands
# NVIDIA GPU
uv pip install vllm --torch-backend=auto
# AMD ROCm
uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/
# CPU
uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly/cpu/
Server Start Commands
# Basic server
vllm serve Qwen/Qwen2.5-1.5B-Instruct
# With tensor parallelism
vllm serve meta-llama/Llama-3.1-70B-Instruct --tensor-parallel-size 4
# With quantization
vllm serve Qwen/Qwen2.5-1.5B-Instruct --quantization fp8
# With GPU memory control
vllm serve Qwen/Qwen2.5-1.5B-Instruct --gpu-memory-utilization 0.8 --swap-space 8
Common API Endpoints
| Endpoint | Method | Description |
|---|---|---|
| /v1/models | GET | List available models |
| /v1/completions | POST | Text completion |
| /v1/chat/completions | POST | Chat completion |
| /health | GET | Health check |
| /metrics | GET | Prometheus metrics |
Key Parameters
| Parameter | Description | Typical Value |
|---|---|---|
| tensor_parallel_size | Number of GPUs for model sharding | 1-8 |
| gpu_memory_utilization | Fraction of GPU memory for KV cache (default 0.92) | 0.7-0.95 |
| swap_space | Removed in current vLLM; use --kv-offloading-size |
n/a |
| max_model_len | Maximum context length | auto |
| max_num_batched_tokens | Max tokens per batch | 2048-8192 |
| max_num_seqs | Max sequences per batch | 128-512 |
Quantization Methods
| Method | GPU Support | Description |
|---|---|---|
| FP8 | Hopper, Ada (native); Ampere W8A16 via Marlin; RDNA via emulation | FP8 E4M3 inference |
| INT8 | Most GPUs | Weight-only INT8 |
| GPTQ | Most GPUs (NVIDIA; limited on ROCm) | Post-training GPTQ |
| AWQ | Turing+ (NVIDIA) | Activation-aware weight quantization |
| GGUF | CPU, GPU | General GPU unification format |