Make your llama generation time fly with AWS Inferentia2

Hugging Face just closed a gap that had been nagging at anyone trying to run large language models on AWS's custom silicon. Inferentia2, the company's second-gen inference chip, could already handle vision and standard text tasks through optimum-neuron. But actual text generation with a proper LLM was missing. Now it's here, and Llama 2 is the model they chose to prove it works.

The workflow is refreshingly boring in the good sense. You compile a model once with NeuronModelForCausalLM, specifying core count and precision, save it, and optionally push it to the Hub so nobody else on your team has to repeat the compile step, which can take anywhere from a few minutes to over an hour depending on size and hardware. From there, generation runs through the familiar transformers API, or through an even simpler pipeline call if you just want text out the other end without touching tokenizers directly.

The numbers are where this gets interesting. On an inf2.48xlarge instance, a latency-tuned Llama 2 7B model encodes 256 input tokens in half a second and reaches a full 1024-token sequence in 2.3 seconds. Bump to 13B and you're still under four seconds end to end. Throughput-tuned configurations, which serve four requests at once, actually push more tokens per second overall — 750 tokens/sec for the 7B throughput variant versus 227 for the latency one at 256 new tokens — because parallel serving amortizes the fixed encoding cost across more requests.

Even the cheap option holds up. A 7B model squeezed onto a single-core inf2.xlarge, the kind of instance you'd rent for a side project rather than a production fleet, still generates around 22-32 tokens per second. That's slower, sure, but Hugging Face points out the average human reads about five words a second, so even the budget tier comfortably outpaces a person scrolling through streamed output.

What's notably absent is any claim of a finished product. The team flags that throughput scaling is currently bottlenecked by device memory and fixed batch sizes, and that Inferentia2's static sequence-length compilation caps how much context you can throw at these models. They float attention sinks as a possible fix for long-context handling, which tells you this is very much a first working version rather than a mature pipeline.