Methodology
Make each capacity boundary visible.
The planners use a shared model profile, then apply the memory and parallelism rules that matter for inference, vLLM serving, and adapter training.
Imported model profile
Public config → parameters, layers, hidden size, and KV heads
A model ID import reads the public Hugging Face configuration and carries the architecture into the planner. The shared link retains the imported values, so a planning brief remains reproducible without a model lookup.
Inference and vLLM
Weights + request-driven KV Cache + runtime reserve
Inference capacity starts with loaded weights, then adds KV Cache from layers, KV heads, head dimension, context, precision, and active requests. Tensor parallelism divides one engine across GPUs. Data parallelism creates independent engines, so the vLLM page keeps max sequences per engine separate from endpoint-wide capacity.
LoRA and QLoRA
Frozen base + adapter target scope + optimizer + activations
The training planner derives adapter parameters from rank, hidden size, layers, and target scope. Sequence length and micro-batch drive the activation term. Gradient checkpointing uses a lower activation profile to represent the recomputation-for-memory tradeoff.
Training topology
Single GPU and DDP replicate; FSDP shards model state
Single-GPU and DDP plans assess the full training state per device. The FSDP plan shards base, adapter, and optimizer state across the selected GPU count while retaining the request-sized activation load on each device.