AI infrastructure, measured on your workload.
Training and inference engineering on AWS Trainium and Inferentia with the Neuron SDK, on GPUs when they are the right answer, and on Bedrock when you should not be running the hardware at all.
The problem
Inference is now the largest line in most AI budgets, and most of it runs on hardware chosen when there was no alternative. AWS designs the Trainium and Inferentia accelerators, the NeuronLink interconnect and the Neuron compiler together, which changes the economics for a large share of workloads. Not all of them.
We port the model, benchmark it against your current estate with identical prompts and traffic, and report the numbers. When Neuron wins, we migrate. When it does not, we say so, and you keep the report.
What we deliver
- Price-performance benchmark: cost per million tokens, throughput, latency, quality parity
- Neuron compilation and NeuronX Distributed sharding of your models
- Serving architecture on SageMaker, EKS or Bedrock custom model import
- Capacity plan and cost model for the next twelve months
- Fine-tuning and distillation pipelines on Trainium
- GPU estate review where GPUs remain the right choice
How we work
Profile
Current cost per million tokens, p95 latency and utilisation. The baseline every later decision refers to.
Compile
Port with Neuron, resolve unsupported operators, shard with NeuronX Distributed. Most problems appear here.
Benchmark
Identical prompts and traffic shape, side by side, with quality checked against your evaluation suite.
Transition
Canary traffic, watch the traces, migrate the rest. GPU capacity returns to the workloads that need it.
What backs it
Common questions
Which models work on Neuron?
Most transformer architectures compile cleanly, including the Llama, Mistral, Qwen and Nova families and standard embedding models. Unusual operators or custom kernels may not; the Compile step finds out in days, not months.
What if our models change every week?
A fast retraining cycle can favour GPUs because every change needs recompiling. We tell you that at Profile stage rather than after a migration.
Do you also work on Azure?
For agents and generative systems, yes. The Silicon practice is AWS-specific because the hardware is.
Related services
Talk to an engineer about this
Thirty minutes, no slides. Bring the workload and we will tell you what we would do and what it would cost.