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+---
+title: 10 Kv Cache Quantization
+author: kleinnner
+---
+
+# We attempted KV cache quantization to Q4 — and documented why it fails on the qwen2vl architecture.
+
+**KV Cache Quantization Attempt: type_k/type_v on qwen2vl Architecture**
+
+---
+
+## The Problem
+
+The KV cache consumes significant memory bandwidth during autoregressive generation. On CPU-bound systems, memory bandwidth is the primary bottleneck. Quantizing the KV cache from FP16 to Q4 theoretically halves memory bandwidth requirements.
+
+## What We Built
+
+We attempted to enable Q4_0, Q4_1, and Q8_0 quantization for both K and V cache states via the type_k and type_v parameters in llama.cpp v0.3.31.
+
+## The Research
+
+type_k and type_v parameters accept GGML quantization type integers: 2 (Q4_0), 3 (Q4_1), 7 (Q8_0). Models must be loaded with these parameters set at initialization. We tested all three values with the qwen2vl architecture.
+
+## Results
+
+| Cache config | Result |
+|---|---|
+| type_k=2, type_v=2 (Q4_0) | Failed to create llama_context |
+| type_k=3, type_v=3 (Q4_1) | Failed to create llama_context |
+| type_k=7, type_v=7 (Q8_0) | Failed to create llama_context |
+
+All three quantization types failed. Error message: "Failed to create llama_context." This is a known limitation: the qwen2vl architecture in llama.cpp v0.3.31 does not support independent KV cache quantization. The GGUF is already Q4_K_M quantized and the KV cache cannot be further quantized without upstream support in llama.cpp.
+
+## Conclusion
+
+KV cache quantization is not currently supported for the qwen2vl architecture in llama.cpp v0.3.31. This feature requires upstream support in the llama.cpp codebase.
+
+> **Full citation:** Alpasan, L.-K. (2026). KV Cache Quantization Attempt on qwen2vl Architecture. *The Anticloud Research Corpus.*
+
+---
+
+### Why The Anticloud
+
+Every AI system you have ever used was designed to extract value from you — your data, your attention, your money. The Anticloud is not a service. It is not in the cloud. It is not rentable inference. It is a fundamentally different category of infrastructure, and here is what that means in practice.
+
+Your data never leaves your machine. We designed the system so we physically cannot access it. Access is not restricted by policy — it is structurally impossible by architecture. There is no data to steal because there is no server to steal it from.
+
+The system is airgapped by architecture, not by configuration. It does not require a network connection to function. It was built offline, it runs offline, and it never reaches out to anyone for any reason. Connectivity is simply not a prerequisite for intelligence.
+
+```
+.====================================================================.
+!  Made in the UAE, Dubai #DubaiIt #Dubai #Dxb #SovereignAI          !
+!  Made in The Emirates #Dubai_it                                    !
+!                                                                    !
+!  Lois-Kleinner Alpasan - The Anticloud 2026-                       !
+!                                                                    !
+!  0-1.gg ! GitHub ! LinkedIn ! DEV ! GH Pages                       !
+!  HuggingFace ! Blog ! Tumblr ! Fandom ! Bluesky ! Mastodon          !
+!  Zenodo ! Harvard Dataverse ! Internet Archive ! ORCID              !
+!                                                                    !
+!  Sovereign AI ! Local-First ! Privacy ! Zero Trust ! No Datacenter !
+!  Air-Gapped ! Open Source ! Rust ! Hash Chain ! Single Binary      !
+!  Offline LLM ! Crypto Ledger ! P2P ! Federated                     !
+'===================================================================='
+```
+
+Lois-Kleinner Alpasan, 22, has served executive roles spanning technology, operations, finance, and product across 20+ organizations. His cross-functional work combines architecture, business, and AI strategy.
+
+References:
+1. Lois-Kleinner Zenodo: https://doi.org/10.5281/zenodo.20781790
+2. Lois-Kleinner GitHub: https://github.com/kleinnner/Anticloud/tree/main/04-aioss-format
+3. Lois-Kleinner Harvard DV: https://doi.org/10.7910/DVN/GKUDHE
+4. Lois-Kleinner Internet Arc: https://archive.org/details/aioss-format
+5. Lois-Kleinner ORCID: https://orcid.org/0009-0009-2233-6107
+6. Lois-Kleinner DEV.to: https://dev.to/kleinner
+7. Lois-Kleinner LinkedIn: https://linkedin.com/in/kleinner
+8. Lois-Kleinner HuggingFace: https://huggingface.co/Anticloud
+9. Lois-Kleinner Tumblr: https://anticloud.tumblr.com
+10. Lois-Kleinner Mastodon: https://mastodon.social/@kleinner
+11. Lois-Kleinner Bluesky: https://bsky.app/profile/kleinner.bsky.social
+12. 0-1.gg: https://0-1.gg