Achieving 2000 Tokens Per Second with QWEN 3.5 27B on RTX-5090

Real-world inference performance remains a critical metric for evaluating local LLM viability, and this benchmark demonstrates substantial throughput gains with QWEN 3.5 27B achieving 2000 tokens per second on consumer hardware. The author tuned settings specifically for document classification—a scenario with high input token counts, minimal caching, and few output tokens—providing valuable real-world performance data for practitioners considering similar use cases.

This result is particularly significant for the local LLM community because it demonstrates that consumer-grade RTX-5090 hardware can deliver production-grade throughput for classification and analytics workloads. The situational nature of these benchmarks (heavily input-focused, low output) underscores an important principle: inference optimization depends fundamentally on workload characteristics, not just model size or hardware.

For teams deploying local models at scale, these numbers validate the viability of using mid-range consumer GPUs for latency-insensitive batch processing and document analysis tasks, potentially offering substantial cost savings compared to cloud-based alternatives.

Source: r/LocalLLaMA · Relevance: 8/10