How Palantir AIP Accelerates Data Migration
The Octopus Model for Enterprise Transformation Enterprise data migration can be among the most costly, complex, and time-consuming endeavors organizations undertake — but it doesn’t have to be. Traditional migrations require coordinating consultants alongside separated internal business and technology teams to unlock the potential of data stored in brittle ERP system, customized SAP legacy instances, or even decentralized SQL systems. The result: siloed transition efforts that can be unwieldy to manage, let alone improve or verify. Palantir AIP introduces a fundamentally different approach that enables organizations to maintain complete contextual awareness across the entire migration lifecycle while deploying AI-accelerated workflows to match SME expectations at each phase. This allows c
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modelupdateproductAPEX MoE quantized models boost with 33% faster inference and TurboQuant (14% of speedup in prompt processing)
I've just released APEX (Adaptive Precision for EXpert Models): a novel MoE quantization technique that outperforms Unsloth Dynamic 2.0 on accuracy while being 2x smaller for MoE architectures. Benchmarked on Qwen3.5-35B-A3B, but the method applies to any MoE model. Half the size of Q8. Perplexity comparable to F16. Works with stock llama.cpp with no patches. Open source (of course!), with github.com/mudler/LocalAI team! https://preview.redd.it/uv2bnfheymsg1.jpg?width=1632 format=pjpg auto=webp s=3eca979e8f9ca6b75d206eecdf29308b74aed530 Perplexity by itself doesn't say the full story. KL divergence tells a story perplexity doesn't: https://preview.redd.it/jn9ua2ksymsg1.jpg?width=1617 format=pjpg auto=webp s=7df969308e10aa6b6d31098c92fca1c14bb42a40 Tiers for every GPU: - I-Quality: 21.3 GB
Running SmolLM2‑360M on a Samsung Galaxy Watch 4 (380MB RAM) – 74% RAM reduction in llama.cpp
I’ve got SmolLM2‑360M running on a Samsung Galaxy Watch 4 Classic (about 380MB free RAM) by tweaking llama.cpp and the underlying ggml memory model. By default, the model was being loaded twice in RAM: once via the APK’s mmap page cache and again via ggml’s tensor allocations, peaking at 524MB for a 270MB model. The fix: I pass host_ptr into llama_model_params , so CPU tensors point directly into the mmap region and only Vulkan tensors are copied. On real hardware this gives: Peak RAM: 524MB → 142MB (74% reduction) First boot: 19s → 11s Second boot: ~2.5s (mmap + KV cache warm) Code: https://github.com/Perinban/llama.cpp/tree/axon‑dev Longer write‑up with VmRSS traces and design notes: https://www.linkedin.com/posts/perinban-parameshwaran_machinelearning-llm-embeddedai-activity-74453741179
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