Vllm
Vllm is an open-source inference engine designed to optimize the deployment and serving of large language models with a focus on throughput and memory efficiency. It implements paged attention, a key optimization that manages key-value cache allocation in fixed-size pages similar to virtual memory systems, reducing memory fragmentation and enabling higher batch sizes during inference.
Performance and Memory Trade-offs
The choice between full precision and quantized model variants represents a fundamental trade-off in LLM deployment. Full precision models (typically FP32 or FP16) maintain higher accuracy but require substantially more GPU memory and bandwidth, limiting batch size and throughput. Quantized variants, such as INT8 or INT4 representations, reduce memory consumption and increase computational speed, though at the cost of some accuracy degradation that varies by model and quantization method.
For models like Qwen 3.6-35B, running in full precision on typical hardware may restrict concurrent requests, while quantized versions enable larger batches and faster per-token generation. The optimal choice depends on specific deployment requirements: latency-sensitive applications may prioritize full precision despite memory constraints, while throughput-focused serving may favor quantized approaches. Vllm supports both configurations, allowing practitioners to measure actual performance characteristics for their target hardware and workload before committing to production deployment.