--- title: "Undominated · MoE Activation Memory Footprint Audit: review, setup & limitations · Undominated.ai" canonical: https://undominated.ai/skills/undominated-moe-activation-memory-footprint-audit/ description: "Calculates active versus total parameter memory footprint and expert weight swapping overhead on constrained GPU nodes." --- # Undominated · MoE Activation Memory Footprint Audit: review, setup & limitations · Undominated.ai > Calculates active versus total parameter memory footprint and expert weight swapping overhead on constrained GPU nodes. [← Explore all skills](/skills/) EVIDENCE AND EVALUATION / Undominated.ai # Undominated · MoE Activation Memory Footprint Audit Calculates active versus total parameter memory footprint and expert weight swapping overhead on constrained GPU nodes. [See setup guidance ↓](#setup)[Original source ↗](https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/definition.md) SOURCE REVIEW Reviewed 2026-10-14 Evidence 3 linked sources Publisher Undominated.ai Licence [MIT ↗](https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/LICENSE.txt) Revision d9cbc7779425 [Read what was—and wasn’t—checked ↓](#review) “Calculates active versus total parameter memory footprint and expert weight swapping overhead on constrained GPU nodes.” [Undominated.ai · upstream description ↗](https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/definition.md) Our analysis follows below. 01 / THE REASONING ## Why this made the selection. - Pairs a bounded workflow with an offline Python checker and explicitly synthetic example inputs. - Created for recurring evidence and comparison failures: missing facts remain unknown and every conclusion keeps its scope. - It names a concrete failure mode: Size VRAM allocations for MoE models when only a subset of experts fit into high-speed memory. ### A good fit for - Size VRAM allocations for MoE models when only a subset of experts fit into high-speed memory. - Run the local checker against a supplied file for this stated condition: FAILED: Remaining VRAM headroom {headroom} GB below required {min_head} GB. ### Weigh up before choosing - Deterministic memory sizing. Assumes steady-state layer distribution. - Fixture checks establish the documented local behaviour, not performance on an unrestricted set of real tasks. - Public GitHub source availability is not yet verified; the complete download contains the reviewed source. - A new npm install path is not yet verified. Use the complete source download. - `2`: Schema error or invalid usage. 02 / THE REVIEW RECORD ## What we actually inspected. Source review has boundaries. A clear record is more useful than a “safe” badge. ### Material inspected - skills/undominated-moe-activation-memory-footprint-audit/SKILL.md - skills/undominated-moe-activation-memory-footprint-audit/scripts/check.py - skills/undominated-moe-activation-memory-footprint-audit/examples/synthetic.json ### Our findings - First-party resource authored by Undominated.ai; this is our own verification record, not an independent endorsement. - The download preserves the complete source and supporting files. Its manifest hashes identify the exact bytes. - The checker names its required fields as: req = ["total_params_b", "active_params_b", "bytes_per_param", "available_vram_gb", "min_headroom_gb"]. - The checker rejects the run rather than passing: FAILED: Remaining VRAM headroom {headroom} GB below required {min_head} GB. ### Not established by this review - Model compliance with these instructions on arbitrary tasks - Native integration in every agent host - Public GitHub source identity - Remote installation through the standard Skills CLI - The new npm resource CLI installation The review applies to the material and revision named here. A newer upstream release can change its behavior. 03 / PUT IT TO WORK ## Add a skill to your workflow. [Upstream setup instructions ↗](https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/definition.md) - Download the complete bundle from https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/bundle.zip and extract it into a new directory. - Place the extracted directory at .agents/skills/undominated-moe-activation-memory-footprint-audit/ or your host's documented skills directory. Preserve SKILL.md, scripts, examples and licence files together. - Run the documented synthetic example from the skill directory with Python 3.10 or later before using your own evidence. ### Before you start - An Agent Skills compatible host - Python 3.10 or later for the optional checker ### Compatibility Agent Skills compatible hosts · Python 3.10+ ### Implementation Python · Markdown ### Local, explicit inputs - read:user-selected-local-file ### Cost model MIT source at no charge. Your agent host or model provider may charge for use; the included offline checks require no paid API. THE COMPLETE REVIEWED DEFINITION ## Read it before you reuse it. Original source bytes, with attribution. Review the host-specific setup notes above. Copy definition ↗ [Download complete resource + licence ↗](/resources/skills/undominated-moe-activation-memory-footprint-audit/bundle.zip)[Raw Markdown ↗](/resources/skills/undominated-moe-activation-memory-footprint-audit/definition.md) # MoE Activation Memory Footprint Audit ## Description Calculates active versus total parameter memory footprint and expert weight swapping overhead on constrained GPU nodes. ## Category `serving` ## Tags `moe`, `memory`, `vram`, `footprint`, `expert-cache` ## When to Use Size VRAM allocations for MoE models when only a subset of experts fit into high-speed memory. ## Operational Limitations Deterministic memory sizing. Assumes steady-state layer distribution. ## Verification Script Run the deterministic verification checker: ```bash python3 scripts/check.py examples/synthetic.json ``` Exit codes: - `0`: Check passed and thresholds satisfied. - `1`: Threshold breach or operational violation detected. - `2`: Schema error or invalid usage. The download contains SKILL.md . Extract the whole bundle; the supporting files are required. Inspect the included MANIFEST.json for file hashes. By **Undominated.ai**. [Exact upstream source ↗](https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/definition.md) · [Licence](/resources/skills/undominated-moe-activation-memory-footprint-audit/LICENSE.txt) · [Attribution](/resources/skills/undominated-moe-activation-memory-footprint-audit/ATTRIBUTION.txt) SHA-256 d9cbc77794253a0e04ab8d99c40908a3f9430960cdb483e939fc8d1a826a6a4f Read the applicable licence MIT License Copyright (c) 2026 Undominated.ai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 04 / FOLLOW THE EVIDENCE ## The source trail. Our notes are separate from the original resource. Check upstream before adopting a new version. - [Original Undominated definition ↗](https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/definition.md) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/definition.md Supports: Workflow and input requirements, Stated limitations - [MIT licence ↗](https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/LICENSE.txt) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/LICENSE.txt Supports: Redistribution terms and attribution - [Complete source bundle ↗](https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/bundle.zip) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-moe-activation-memory-footprint-audit/bundle.zip Supports: Full local source, supporting files and integrity manifest KEEP COMPARING ## Other approaches to consider. Related by category or shared topics. These are alternatives to inspect, not a measured quality order. [### Undominated · Activation Recomputation Tax Audit ↗ Audits selective vs full gradient checkpointing memory savings vs compute FLOP taxes for long-context prefill.](/skills/undominated-activation-recomputation-tax-audit/)[### Undominated · KV Cache Memory Sizing Audit ↗ Audits KV cache memory footprint per token and max concurrent sequence limits under FP16, FP8, and INT4 KV caches to prevent serving OOMs.](/skills/undominated-kv-cache-sizing-audit/)[### Undominated · Cross modal KV allocation audit ↗ Audits visual token versus text token KV cache allocation ratios in long multimodal conversations.](/skills/undominated-cross-modal-kv-allocation-audit/) [AI Tools ↗](/tools/)[Skills ↗](/skills/)[Agents ↗](/agents/)[MCP Servers ↗](/mcp-servers/)[Workflows ↗](/workflows/) ## Continue your investigation - [Choose a toolkit](/tools/) - [Inspect execution hosts](/agents/) - [Check tool access](/mcp-servers/) - [Check project dependencies](/check-repo/)