GPU Benchmarking

A developer has achieved a breakthrough in local LLM performance by pairing the new Blackwell-based RTX 5080 with a legacy RTX 3090, pushing the Qwen 3.6 27B (Q8) model to an impressive inference speed of over 80 tokens per second. ▶ Heterogeneous Synergy: By leveraging the high-bandwidth GDDR7 of the RTX 5080 alongside the 24GB VRAM of the RTX 3090, this setup effectively bypasses the memory capacity limitations of mid-tier consumer cards while maintaining elite throughput. ▶ The 27B "Sweet Spot": Qwen 3.6 27B at Q8 quantization delivers high-fidelity output at speeds that rival or exceed premium cloud APIs, making it a viable candidate for high-performance local RAG and autonomous agent workflows. Bagua Insight This benchmark underscores a critical reality in the GenAI era: Memory Bandwidth is King. While the RTX 5080 has been criticized for its 16GB VRAM ceiling, its GDDR7 architecture provides the massive throughput necessary to saturate the compute engines during inference. The "Frankenstein" approach—mixing generations—proves that the secondary market for high-VRAM legacy cards (like the 3090) remains a vital pillar for the AI developer ecosystem. We are seeing a shift where local "prosumer" hardware is no longer just for testing, but capable of production-grade performance for models in the 30B parameter range. Actionable Advice 1. Hardware Strategy: When building local AI workstations, prioritize an asymmetric GPU configuration. Pairing a high-bandwidth primary card (50-series) with a high-capacity secondary card (3090/4090) offers the best ROI for running quantized models without the enterprise price tag. 2. Model Optimization: Target models in the 20B-35B range for local deployment. These models, when run at Q8 precision, hit the performance sweet spot for dual-GPU setups, offering a balance of reasoning capability and near-instantaneous response times. 3. Stack Tuning: Utilize inference engines like llama.cpp or vLLM that allow for granular control over layer distribution. Manually offloading compute-heavy layers to the GDDR7-equipped card while using the older VRAM for weight storage is the key to hitting these high-throughput numbers.