Building a 10.2x Faster Search-Only Retrieval Path on Arm64
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NeonRecall's best accepted native Arm64 workload showed a 10.214x three-run median throughput speedup over its FP32 retrieval baseline. That number needs a clear boundary. This is a single-threaded, search-only benchmark over already-produced embeddings. It does not include model inference, embedding generation, index construction, or corpus and query quantization. Within that boundary, the result was consistent across three vector widths: Workload Per-run speedups Three-run median INT8 / FP32…
1Key Takeaways
- NeonRecall's best accepted native Arm64 workload showed a 10.214x three-run median throughput speedup over its FP32 retrieval baseline.
- This is a single-threaded, search-only benchmark over already-produced embeddings.
- It does not include model inference, embedding generation, index construction, or corpus and query quantization.
- Within that boundary, the result was consistent across three vector widths: Workload Per-run speedups Three-run median INT8 / FP32….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that neonRecall's best accepted native Arm64 workload showed a 10.214x three-run median throughput speedup over its FP32 retrieval baseline.
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