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Apache Arrow and Apache DataFusion Now List ArrowMetal: Arrow Compute and a DataFusion Optimizer Rule on the Apple Silicon GPU
Data Engineering10 min read

Apache Arrow and Apache DataFusion Now List ArrowMetal: Arrow Compute and a DataFusion Optimizer Rule on the Apple Silicon GPU

ArrowMetal is now on Apache Arrow's Powered By page and on Apache DataFusion's integrations list. What the two listings mean, and what the listed thing does as of 0.5.0, with one real run on each side.

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DataFusion on the Apple Silicon GPU: One Optimizer Rule, the Same SQL, Sorts 6.9x to 28.8x Faster
Data Engineering10 min read

DataFusion on the Apple Silicon GPU: One Optimizer Rule, the Same SQL, Sorts 6.9x to 28.8x Faster

Apache DataFusion runs any physical optimizer rule you register. ArrowMetal 0.4.0 ships one for the Apple silicon GPU: the SQL is unchanged, the answers are DataFusion's, and full sorts of 250,000 to 50 million rows run 6.9x to 28.8x faster on an M4 Max. What it takes, what it leaves, and why.

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pankhllm: The LLM Gateway That Learns to Skip the LLM, Without Replacing the Stack You Already Run
Data Engineering10 min read

pankhllm: The LLM Gateway That Learns to Skip the LLM, Without Replacing the Stack You Already Run

Most of an agent's LLM calls are not writing anything. They are decisions: which tool, which skill, which parameters, made thousands of times a day by a model paid in seconds and tokens. pankhllm sits where your app already calls an LLM, learns those decisions from its own traffic, and starts making them in 0.2 ms on a CPU with a 262 KB model. What it is not sure about still goes to your LLM. On the same 14 questions: a 12B planner 1,743 ms, Laya 49 ms, pankhllm's own model 4 ms, all 14 correct. Here is what it is, what it is not, and where it stops.

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