The Australian data center operator plans to set up 5GW of capacity in India.
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Sharing a small CPU inference benchmark for nvidia/parakeet-tdt-0.6b-v3 that turned up a result I didn't expect going in. Setup: 2 x86-64 vCPUs (AVX2/FMA), 7.7GB RAM, no GPU. Test audio: 16.78s Harvard sentences at 16kHz mono. Results: Inference path RTF Peak Memory CPU utilization HF Transformers bfloat16 0.519 ~430MB delta — ONNX Runtime FP32 (onnx-asr) 0.328 2,667MB 49.9% GGUF Q6_K (parakeet.cpp) 0.708 928MB 99.8% ONNX Runtime is 37% faster than HF Transformers bfloat16 on this hardware. The gap comes from operator fusion and AVX2-optimized execution providers in ONNX Runtime that the PyTorch CPU path doesn't exploit as aggressively. Memory cost is the tradeoff — FP32 weights load at ~2.7GB peak. GGUF Q6_K trades throughput for memory efficiency. 928MB peak vs 2.7GB, but RTF doubles and CPU utilization hits 99.8%. For memory-constrained deployments it's the right call. For sustained throughput on a box with headroom, ONNX wins. One methodological note worth flagging for anyone doing ASR benchmarking with synthetic audio: espeak-ng inflated WER to 20.9% on a sentence set where gTTS got 4.65%. Both runtimes got identical WER within each run, confirming it's the TTS distribution mismatch rather than model or quantization quality. NVIDIA reports 1.93% on LibriSpeech — the gTTS number is a much more honest CPU-only proxy. Github repo with code, raw results, and evaluation scripts in comments below. Disclosure: benchmark was run using Neo, a local AI engineering agent inside Claude Code using its MCP. Mentioning because the runtime and audio choices came from its research phase, not prior knowledge on my end. submitted by /u/gvij [link] [留言]
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