VRAM workload planner

Move from model to workload to hardware.

Import a model configuration or set a planning profile, choose what the model must do, then make weight format, context pressure, simultaneous requests, and per-GPU capacity visible in one path.

Model and workload planner

Plan the workload before choosing the VRAM tier.

Start with local chat, coding, RAG, API serving, LoRA, or QLoRA, then keep the relevant memory pressure and hardware boundary visible.

How the estimates work →

The values below are editable planning baselines. Change the few inputs you know; open Advanced assumptions only when they change your decision.

Model configuration

Import a Hugging Face model profile.

Load a public model revision to set its verified parameter total and decoder attention shape. The current workload context stays unchanged.

Current hardware

No hardware profile saved.

Save the GPU capacity you already have, then apply it across VRAM planning paths.

Advanced assumptionsAdjust architecture, cache precision, and multi-GPU shape when the primary workload is defined.
Loaded weights——
KV Cache at target——
Required per GPU——
Capacity result——

Memory analysis

See what consumes the per-GPU capacity.

Compare the same memory-capacity model across workload pressure and weight formats, with the current planning point kept visible.

Planned with margin—
Usable per GPU—
  • ——
  • ——
  • ——
  • ——
  • ——

—

Pressure matrix

Context × simultaneous requests

Each cell shows required VRAM per GPU. The outlined cell is the current plan.

Requests ↓ / context →————
—————————
—————————
—————————
—————————

Weight-format comparison

Keep the workload constant.

Weights change between rows; architecture, KV Cache pressure, runtime reserve, and GPU topology stay fixed.

Weight formatWeights / GPURequired / GPUAfter margin
————
————
————

Planning brief

Turn the model and workload into the next hardware decision.

—

Next decision

Verify the local runtime boundaryReview workload guidesDiagnose an observed OOM

Plan tools

Keep this useful version close, shareable, and easy to revisit.

Reset inputs

Optional support

Keep practical planning tools available.

If this result made the next decision clearer, an optional contribution helps cover hosting, source reviews, and continued improvements.

Choose a small tip on Ko-fi

Model baseline

Keep parameter count, exact imported revision, architecture-sensitive KV Cache, and weight format together.

Workload branch

The selected intent decides whether the next step is local inference verification, vLLM serving, LoRA, or QLoRA.

Hardware boundary

Compare weights, cache, runtime reserve, and implementation margin with usable memory per GPU before selecting capacity.