Inference and serving
Latency, throughput and cost per request: batching, quantization, caching and the right serving stack.
SHEBANG: Future Ready
GPU platforms for both training and inference, built on an open stack without lock-in.
Getting a model to answer is easy. Getting it to answer fast, for many users, at a sensible cost and without data leaving the building is engineering. That's where we work, for both training and inference: from architecture and hardware choices to operations. We build on an open-source stack so you can switch model, hardware or vendor without starting over.
Inference engineer · AI infrastructure engineer · ML platform engineer · Training infrastructure · MLOps / LLMOps · AI solutions architect · GPU cluster · LLM serving · Kubernetes for AI · CUDA · ROCm
WHAT WE DO
Latency, throughput and cost per request: batching, quantization, caching and the right serving stack.
Infrastructure for pre-training, SFT and other post-training: distributed jobs, checkpoints and efficient GPU use.
From one GPU server to clusters many teams share, on NVIDIA with CUDA or AMD with ROCm, with scheduling, quotas and monitoring.
We focus on the open-source training and inference stack, so you own the platform and aren't tied to a vendor.
Versioning, rollout, evaluation and observability for models in production.
We choose between managed service, self-hosting and hybrid based on data, requirements and cost, independent of vendor.
Not sure where to start? Let's work it out together.
RIGHT FOR YOU WHEN
TOOLS WE USE
CUDA · ROCm · Kubernetes · llm-d · vLLM · SGLang · NVIDIA Triton · Ray · PyTorch · DeepSpeed · Burn · Candle · Prometheus
LET'S START WITH A CONVERSATION
Tell us what you want to achieve and one of our specialists will get in touch. There's no commitment.

Ronnie Qvist
Head of Sales · SHEBANG
GOOD TO KNOW
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