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Llama 3.2 1B model benchmarked on Qualcomm QCS6490 chip

By PulseAugur Editorial · [1 sources] ·

The author has successfully benchmarked the Llama 3.2 1B Instruct model on Qualcomm's QCS6490 chip, achieving 1,024 tokens/s for prefill and 16.4 tokens/s for decoding. This follows a previous benchmark of the Qwen3 0.6B model on the same hardware, using a generalized workaround to bypass a compiler flag issue in the official AI Hub export script. The method involves manually adjusting compile parameters and pinning input shapes, and the author plans to test larger models like Qwen3-1.7B and Llama 3.2 3B to establish a performance curve for the chip. AI

IMPACT Provides performance data for running smaller LLMs on edge hardware, informing optimization strategies for on-device AI.

RANK_REASON The item reports on performance benchmarks of AI models on specific hardware, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Llama 3.2 1B model benchmarked on Qualcomm QCS6490 chip

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The item reports on performance benchmarks of AI models on specific hardware, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Anguo ·

    The workaround generalizes: Llama 3.2 1B on QCS6490 — 1,024 tok/s prefill, 16.4 tok/s decode

    <h1> The workaround generalizes: Llama 3.2 1B on QCS6490 — 1,024 tok/s prefill, 16.4 tok/s decode </h1> <p>Yesterday I published the first public GenAI numbers for Qualcomm's QCS6490: Qwen3-0.6B at 1,555 tok/s prefill and 24.6 tok/s decode. Getting there took a hand-built chain, …

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