v0.3.0 — Recipe Editor, vLLM Backend, DeepSeek V4

What's New

✏️ Recipe Editor TUI

Browse, create, edit, duplicate, and delete recipes without touching TOML files. Manage GPU tiers and docker images from the same menu. Proper TOML round-trip (tomllib + tomli_w), validation, auto-backup on save.

⚡ vLLM Serving Backend

New provider type for models too large for llama.cpp. Tensor-parallel serving across multi-GPU clusters with automatic GPU detection, FlashInfer attention, FP8 KV cache, and reasoning parser support. Based on the official vllm/vllm-openai:v0.20.1 image.

🧠 DeepSeek V4 Support

• V4-Flash (284B, 13B active): 7 GGUF recipes via llama.cpp + 2 vLLM recipes
• V4-Pro (1.6T, 49B active): 5 vLLM recipes across datacenter clusters
• Custom llama.cpp branch support for models with unmerged upstream PRs
• Split-file GGUF discovery for large sharded models

🖥️ Multi-GPU Cluster Tiers

19 GPU tiers (up from 10), including:

• 2×/4× H100 SXM (160–320 GB)
• 2×/4×/5× H200 SXM (282–705 GB)
• 2×/4× B200 SXM (384–768 GB)
• 8× H100 and 8× A100 clusters

📊 By the Numbers

• 70 recipes across 4 providers (vast_gguf, vLLM, Together AI, local)
• 19 GPU tiers from RTX 4090 to 8×B200 SXM
• ~5,000 lines of Python across 18 modules
• 4 docker images: prebuilt, builder, vLLM, legacy

Dependencies

• Added tomli_w>=1.0.0 for recipe editor TOML write-back

Full Changelog

v0.2.0...v0.3.0