Skip to content

GPU Resident Memory Budget

Add explicit resident weight and runtime components, compare a device budget and reserve, and retain byte-rounding and measurement assumptions.

Use this result well

Inputs that matter
Resident weight parameters, Stored bits per weight, Gradients (MiB), Optimizer state (MiB), and 6 more
Output to expect
Modeled resident memory
  • Check the units and required inputs before comparing results.
  • Keep the assumptions with a copied result so you can reproduce the calculation later.
Was this tool helpful?

Related Tools

Tools you might need next

Batch Tensor Memory Budget

Inspect a fixed-shape linear tensor-storage budget and its whole-batch limit with explicit fixed memory, element size and reserve.

Data Science
Particle Buffer Data Size

Calculate particle buffer bytes from allocated capacity, measured record size, copies and fixed overhead without inferring frame-rate performance.

Game Development
Texture Data and Mip Size

Calculate texture data bytes for RGBA or BC formats by summing exact mip levels and layers, with whole blocks and explicit allocation limits.

Game Development

Reference & details

How it works

GPU resident memory budget

Weight payload bytes = ceil(resident parameters × stored bits/8); modeled resident bytes = weight payload + entered component bytes; planning margin = device budget − resident memory − reserve A component ledger with explicit assumptions. Gradient, optimizer and master-weight precision, offload and sharding vary; none is inferred from weight precision. Packed weight payload excludes per-tensor alignment, quantization scales and metadata; include those in other allocations. Peak components must describe a compatible simultaneous workload. Allocated, reserved and process/device memory readings are not interchangeable. Modeled resident total and reserve round up to whole bytes; the entered available budget rounds down to whole bytes.

Updated: September 2026

Example Scenarios

Add packed weight payload to explicit resident gradients, optimizer, master-weight, activation and runtime memory components. Compare with a chosen device budget and reserve.

Common Mistakes to Avoid

Applying gpu resident memory budget outside its stated assumptions

Gradient, optimizer and master-weight precision, offload and sharding vary; none is inferred from weight precision. Packed weight payload excludes per-tensor alignment, quantization scales and metadata; include those in other allocations. Peak components must describe a compatible simultaneous workload. Allocated, reserved and process/device memory readings are not interchangeable. Modeled resident total and reserve round up to whole bytes; the entered available budget rounds down to whole bytes.

FAQ

No. Some weights/states remain at higher precision and activation/workspace behavior depends on the actual workload.

No. Measure full representative iterations and account for dynamic shapes, warmup and other allocations.

About GPU Resident Memory Budget

Add explicit resident weight and runtime components, compare a device budget and reserve, and retain byte-rounding and measurement assumptions. Choose the mode that matches your measurements or study design, enter the stated units and keep the method and limits with the result.