Fission Labs Model Fine Tuning and Inference Benchmarking on AWS

Frontier models are powerful, but for focused domain tasks you may be paying for capability you do not use. Fission Labs fine tunes models on your proprietary data and benchmarks them on AWS, so you can serve a model that is accurate for your use case at a latency and cost you have measured.

Why fine tuning and benchmarking on AWS Model choices are often made on public leaderboards rather than on your data, prompts, and traffic. AWS gives you the training and serving options to test alternatives properly, from Amazon SageMaker and Amazon Bedrock to GPU and AWS Inferentia instances. Fission Labs, an AWS Advanced Tier Services Partner with the AWS Generative AI Competency, turns those options into a decision backed by numbers.

What you get: a model you have measured

• Data preparation and annotation: cleaning, labeling, and evaluation sets built from your proprietary data, with Amazon SageMaker Ground Truth where human labeling is needed
• Domain fine tuning: RLHF, PEFT, and QLoRA on Amazon SageMaker, or Amazon Bedrock custom models, matched to your accuracy target and budget
• Serving optimization: deployment on vLLM, TensorRT-LLM, and Triton, or managed endpoints, tuned for your traffic profile
• FLOTEST benchmarking: time to first token, goodput, throughput, and cost per token compared across models and serving stacks
• Decision report: a recommended model and serving configuration with the trade offs documented

Proven results: FloTorch FloTorch, the Fission Labs GenAI accelerator available on AWS Marketplace, gives teams a repeatable way to evaluate and optimize GenAI workloads.

• 3X faster time to market
• 40% reduction in GenAI operating costs

Security and governance Training data and model weights stay in your AWS account, encrypted with AWS KMS and controlled through AWS IAM. Evaluation sets, training runs, and benchmark results are versioned, so every model decision can be traced and reproduced.

How we engage

• Assess: define the target task, quality bar, latency, and cost goals
• Prepare: build training and evaluation datasets from your proprietary data
• Fine tune: train candidate models with the right method for your data and budget
• Benchmark: compare models and serving stacks with FLOTEST under realistic load
• Deploy and hand over: serve the selected model with monitoring, documentation, and runbooks

Who this is for

• Teams whose GenAI costs grow faster than usage value
• Organizations with proprietary data and domain specific tasks
• Engineering leaders who need latency and cost evidence before choosing a model

Get started Contact us for a free scoping call to review your use case, data, and performance goals.