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AMEVA-Runtime (Python)

PyPI Python License

Next-Gen Unified On-Device Hardware Orchestration & 6-Modality AI Acceleration Runtime (with BitNet 1.58-bit Vulkan Compute) for Mobile & Edge

Installation

pip install ameva-runtime

Quickstart

import ameva_runtime as ameva
from ameva_runtime import vulkan

# Inspect hardware
profile = ameva.detect_hardware()
print(f"Target: {profile.soc_name} | {profile.gpu_vendor}")

# Run diagnostics
doc = vulkan.Doctor()
report = doc.run_self_test()
print(f"Passed: {report.passed_stages}/{report.total_stages}")

Empirical Benchmarks

  • Galaxy S25 (Adreno 830):
    • BitNet 1.58-bit LLM: 17.558 t/s (12.58x speedup), Prompt Eval 205.9 ms.
    • Qwen2.5-0.5B LLM: 35.80 t/s (25/25 VRAM layers).
    • Whisper STT: 4,401 ms, TTS: RTF 0.264x (medium) / 0.993x (high-fp16).
  • Galaxy A35 (Mali-G68 MP5):
    • BitNet 1.58-bit LLM: 3.471 t/s (5.94x speedup), Prompt Eval 1,552.8 ms.
    • Qwen2.5-0.5B LLM: 4.44 t/s (+26.9% vs NEON).
    • Whisper STT: 360.60s (2.26x speedup), TTS: RTF 1.146x.

Documentation

License

Apache-2.0 License. Copyright (c) 2026 Eunho Kim (@uno-km).

Release files for ameva-runtime 2.7.2

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