Deploying Llama 2 without burning cash or annoying users with lag turns out to be a solvable problem. Hugging Face and AWS just published a benchmark covering over 60 configurations across three model sizes, four instance types, and load levels ranging from 1 to 20 concurrent requests. The goal was simple: figure out where latency, throughput, and cost actually collide.
The headline finding is that 4-bit GPTQ quantization changes the math entirely. With GPTQ, Llama 2 13B runs comfortably on a single GPU—something that would otherwise demand far more hardware. That’s the difference between a g5.2xlarge and a much pricier multi-GPU setup for teams that don’t need maximum throughput. The benchmark measured both tokens per second and milliseconds per token, giving a clear view of the tradeoff curve rather than a single cherry-picked number.
They tested on NVIDIA A10G GPUs via g5 instances and A100s on p4d.24xlarge, using the Hugging Face LLM Inference Container powered by Text Generation Inference. TGI’s dynamic batching and tensor parallelism are doing the heavy lifting here, and it’s already in production at companies like Grammarly, Uber, and Scale AI. The raw data and code are public on GitHub, which means anyone can reproduce the results or adapt them to their own workloads.
The implicit message is that “bigger model, bigger bill” isn’t always the right frame. For real-time services, latency per token matters more than raw throughput, and a quantized 13B on a single A10G may beat an unquantized version on more expensive hardware for many use cases. The authors note that Inferentia2 could shift these economics further, but that’s a benchmark for another day.
