Qwen 0.3 6B Quantized running at 90tok/s on A19 Pro chip (twitter.com)

A community report shows a quantized Qwen 0.3 6B model achieving roughly 90 tokens/sec on an “A19 Pro” inference chip. In short: a relatively small (6‑billion parameter) Qwen variant was quantized and run on specialized hardware, yielding a sustained generation rate in the order of 90 tok/s. The post highlights a practical, low-cost inference pathway for a fully fledged LLM by combining model-size selection, aggressive quantization, and hardware-specific optimization. Why this matters: 90 tok/s for a 6B quantized model indicates that useful interactive LLM throughput is attainable on non-mainstream AI accelerators, lowering latency and cost barriers for deployments outside flagship GPUs. Key technical takeaways are that model quantization (likely int8/int4 or similar) plus runtime/hardware tuning can drastically shrink memory and compute footprints while maintaining usable generation speeds. For practitioners this suggests an attractive trade-off space: deployable LLMs at edge or lower-cost datacenter hardware, faster iteration for productization, and potential batch/latency improvements. Caveats include the usual quantization trade-offs (possible quality degradation, sensitivity to prompts, and the need for task-specific benchmarks), so users should validate accuracy, memory usage, and end-to-end latency on their own workloads.