Service
Inference optimisation & multi-GPU
When an existing deployment is slow, expensive or hitting capacity limits, we profile it and apply targeted changes, from serving configuration to multi-GPU parallelism, then measure the result against the original baseline.
What you get
- A measured before-and-after comparison under the same test conditions
- Higher concurrency or lower latency, depending on your priority
- A clearer picture of when to scale and how
Typical scope
- Profiling of the current serving path
- Batching, caching, quantisation and scheduler tuning
- Tensor / pipeline parallelism across multiple GPUs or nodes
- Autoscaling and capacity planning recommendations
Technical detail
- → Speculative decoding and prefix caching where the workload benefits
- → Separate latency-sensitive and batch traffic where needed
Scope and pricing are agreed per engagement. Implementation is a fixed, one-time fee; infrastructure is paid directly to your provider; ongoing support is optional and priced separately.
Ready to talk through your requirements?
Tell us the use case, where it should run and how many people will use it. We will take it from there.