2 Commits

Author	SHA1	Message	Date
Sebastian Krüger	bb3dabcba7	feat(ai): complete GPU deployment with self-hosted Qwen 2.5 7B model ... This commit finalizes the GPU infrastructure deployment on RunPod: - Added qwen-2.5-7b model to LiteLLM configuration - Self-hosted on RunPod RTX 4090 GPU server - Connected via Tailscale VPN (100.100.108.13:8000) - OpenAI-compatible API endpoint - Rate limits: 1000 RPM, 100k TPM - Marked GPU deployment as COMPLETE in deployment log - vLLM 0.6.4.post1 with custom AsyncLLMEngine server - Qwen/Qwen2.5-7B-Instruct model (14.25 GB) - 85% GPU memory utilization, 4096 context length - Successfully integrated with Open WebUI at ai.pivoine.art Infrastructure: - Provider: RunPod Spot Instance (~$0.50/hr) - GPU: NVIDIA RTX 4090 24GB - Disk: 50GB local SSD + 922TB network volume - VPN: Tailscale (replaces WireGuard due to RunPod UDP restrictions) Model now visible and accessible in Open WebUI for end users. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-11-21 13:18:17 +01:00
Sebastian Krüger	8de88d96ac	docs(ai): add comprehensive GPU setup documentation and configs ... - Add setup guides (SETUP_GUIDE, TAILSCALE_SETUP, DOCKER_GPU_SETUP, etc.) - Add deployment configurations (litellm-config-gpu.yaml, gpu-server-compose.yaml) - Add GPU_DEPLOYMENT_LOG.md with current infrastructure details - Add GPU_EXPANSION_PLAN.md with complete provider comparison - Add deploy-gpu-stack.sh automation script 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-11-21 12:57:06 +01:00