Chimeraforge
LLM deployment planner
Turns "which model, quantization, GPU, and backend — how many, will it fit, will it hit my SLO, what will it cost" into a fast, measured answer from your shell, your Python, or your AI assistant.
uvx chimeraforge plan --model-size 8b --hardware "RTX 4090 24GB"The trust principle
Every number is labeled measured, estimated, or unknown — and the tool refuses to fake the ones it cannot stand behind. VRAM and KV-cache are computed from real model architecture. Throughput is a measured lookup when one exists, otherwise an explicit bandwidth roofline, never dressed up as data. Quality below the bundled corpus reports unknown rather than an invented score, and a zero-result plan names the exact gate that rejected every candidate.
Model-agnostic planning
Plan any registry name, Ollama tag, or HuggingFace repo — not just a bundled list. Tensor-parallel splitting for models too big for one card, and a KV-cache trade-off menu across cost, latency, and quality.
An MCP server
chimeraforge mcp exposes the same planner to Claude, Cursor, and other MCP clients, so an assistant answers capacity questions from measured numbers instead of guessing.
22 GPU profiles
Consumer Ada and Blackwell (RTX 30/40/50-series), datacenter (A100 40/80GB, H100, H200, B200, L4, T4), and AMD MI300X — each with VRAM, bandwidth, FP16 TFLOPS, TDP, and interconnect.
Commands
11 commands. Full flags and output samples live in the README.
planpredictive capacity planner
suggestdiscover and rank models that fit
measurebenchmark live, plan on real numbers
cataloglocal model catalog
safetylive refusal screen
benchlive inference benchmarking
evalquality evaluation
comparediff benchmark runs
refitupdate planner coefficients
reportgenerate reports
mcpserve the planner to AI assistants
Evidence
Each capability traces to the measurements behind it. These are the published reports, not a summary of them.
Capacity planning
The predictive planner and its coefficients come out of the optimization phase — KV-cache tuning, context scaling, and the capacity-planner report itself.
KV-cache precision
The standardized FP16-versus-FP8 KV-cache battery behind the quantized-cache guidance.
What it does not do
The limits the tool states about itself.
- MoE active-versus-total parameter divergence, reasoning tokens, speculative decoding, and prefix caching are not modeled yet.
- Quantization coverage for vLLM and TGI is GGUF-only so far.
- Tensor- and pipeline-parallel throughput are comms-modeled estimates, not measured, and cannot be combined in a single plan.
- The bundled corpus is fit primarily on one rig (RTX 4080 12GB); other GPUs scale from bandwidth and compute until you run measure.