--- title: "Undominated · Multi-Head Latent Attention Audit: review, setup & limitations · Undominated.ai" canonical: https://undominated.ai/skills/undominated-mla-latent-dimension-audit/ description: "Calculates DeepSeek Multi-Head Latent Attention (MLA) low-rank compression factors and verifies KV cache reduction relative to MHA and GQA." --- # Undominated · Multi-Head Latent Attention Audit: review, setup & limitations · Undominated.ai > Calculates DeepSeek Multi-Head Latent Attention (MLA) low-rank compression factors and verifies KV cache reduction relative to MHA and GQA. [← Explore all skills](/skills/) EVIDENCE AND EVALUATION / Undominated.ai # Undominated · Multi-Head Latent Attention Audit Calculates DeepSeek Multi-Head Latent Attention (MLA) low-rank compression factors and verifies KV cache reduction relative to MHA and GQA. [See setup guidance ↓](#setup)[Original source ↗](https://undominated.ai/resources/skills/undominated-mla-latent-dimension-audit/definition.md) SOURCE REVIEW Reviewed 2026-10-14 Evidence 3 linked sources Publisher Undominated.ai Licence [MIT ↗](https://undominated.ai/resources/skills/undominated-mla-latent-dimension-audit/LICENSE.txt) Revision 9b7bf5d1fa96 [Read what was—and wasn’t—checked ↓](#review) “Calculates DeepSeek Multi-Head Latent Attention (MLA) low-rank compression factors and verifies KV cache reduction relative to MHA and GQA.” [Undominated.ai · upstream description ↗](https://undominated.ai/resources/skills/undominated-mla-latent-dimension-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: Pure mathematical verification of KV projection tensor sizes and per-token memory footprint. ### A good fit for - Audit architectural memory savings of models using low-rank key/value compression (DeepSeek V2/V3/R1). - Run the local checker against a supplied file for this stated condition: FAILED: MLA compression ratio {ratio_rounded}x is below required {min_comp}x. ### Weigh up before choosing - Pure mathematical verification of KV projection tensor sizes and per-token memory footprint. - 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-mla-latent-dimension-audit/SKILL.md - skills/undominated-mla-latent-dimension-audit/scripts/check.py - skills/undominated-mla-latent-dimension-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: required = ["num_layers", "num_heads", "head_dim", "kv_latent_dim", "rope_dim", "bytes_per_elem", "gqa_kv_heads", "min_required_compression_ratio"]. - The checker rejects the run rather than passing: FAILED: MLA compression ratio {ratio_rounded}x is below required {min_comp}x. ### 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-mla-latent-dimension-audit/definition.md) - Download the complete bundle from https://undominated.ai/resources/skills/undominated-mla-latent-dimension-audit/bundle.zip and extract it into a new directory. - Place the extracted directory at .agents/skills/undominated-mla-latent-dimension-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-mla-latent-dimension-audit/bundle.zip)[Raw Markdown ↗](/resources/skills/undominated-mla-latent-dimension-audit/definition.md) # Multi-Head Latent Attention Audit ## Description Calculates DeepSeek Multi-Head Latent Attention (MLA) low-rank compression factors and verifies KV cache reduction relative to MHA and GQA. ## Category `architecture` ## Tags `mla`, `deepseek`, `attention`, `kv-cache`, `compression` ## When to Use Audit architectural memory savings of models using low-rank key/value compression (DeepSeek V2/V3/R1). ## Operational Limitations Pure mathematical verification of KV projection tensor sizes and per-token memory footprint. ## 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-mla-latent-dimension-audit/definition.md) · [Licence](/resources/skills/undominated-mla-latent-dimension-audit/LICENSE.txt) · [Attribution](/resources/skills/undominated-mla-latent-dimension-audit/ATTRIBUTION.txt) SHA-256 9b7bf5d1fa963c11b7d6e1257a7353ad6c99f44a16a66aee26cd3efaf029e2da 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-mla-latent-dimension-audit/definition.md) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-mla-latent-dimension-audit/definition.md Supports: Workflow and input requirements, Stated limitations - [MIT licence ↗](https://undominated.ai/resources/skills/undominated-mla-latent-dimension-audit/LICENSE.txt) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-mla-latent-dimension-audit/LICENSE.txt Supports: Redistribution terms and attribution - [Complete source bundle ↗](https://undominated.ai/resources/skills/undominated-mla-latent-dimension-audit/bundle.zip) Checked 2026-10-07 https://undominated.ai/resources/skills/undominated-mla-latent-dimension-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 · H2O heavy hitter eviction audit ↗ Audits Heavy Hitter Oracle (H2O) KV cache eviction and critical prompt token retention under memory limits.](/skills/undominated-h2o-heavy-hitter-eviction-audit/)[### Undominated · Ring MLA hybrid audit ↗ Audits Ring Attention combined with Multi-Head Latent Attention (MLA) compressed latent states.](/skills/undominated-ring-mla-hybrid-audit/)[### Undominated · SnapKV cluster compression audit ↗ Audits SnapKV prompt attention feature clustering and compressed KV bank generation ratios.](/skills/undominated-snapkv-cluster-compression-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/)