Your AI workload is running. Is it production-ready?
Getting an AI model running is one thing. Getting it to perform reliably at scale, with the throughput, latency and cost profile your business needs, is something else. Our GPU Inference & ML Performance Sprint is a short, fixed-price engagement designed to profile your current setup, identify where performance is being lost, and implement targeted optimisations directly in your environment.
Fixed price. Delivered in 5-10 days. No lengthy engagement to get to the result.
PERFECT FOR
Engineering teams running AI inference or ML training on NVIDIA hardware
Platform teams managing GPU compute in on-prem or edge environments
Organisations with working AI systems that needs to go from “running” to production-ready
Government, research and regulated enterprise environments
A focused sprint. Real changes in your environment.
HOW IT WORKS
01
Days 1-2
Understand and profile your workload under realistic conditions and establish a performance baseline
02
Days 3-4
Implement targeted optimisations directly in your environment, validated against your real workload
03
Day 5
Benchmark against baseline, knowledge transfer, and handover with configuration documentation
04
Investment
Price on application
FAQ
Our workload is inference, not training. Is this still relevant?
Yes. AI inference is the primary focus for most teams coming to us, whether that's large language models, embedding models, or domain-specific AI serving. Training workloads are also in scope if that is where the constraint sits.
We're running on-prem in a regulated environment. Can you work within our security controls?
Yes. We work in on-prem, edge and regulated environments as standard. Data residency, network isolation, and access controls are factored into our engagement.
Will this disrupt our current environment?
Changes are validated before being applied to production workloads wherever possible. We work within your change management processes and make incremental, testable adjustments.
What do we need to provide?
Access to the GPU environment, representative workload data or request traces, and a member of your team to work alongside. We will confirm prerequisites during scoping.
What happens after the sprint?
The sprint delivers an optimised environment with documentation and knowledge transfer. If further work is identified, we can scope a follow-on engagement. There is no obligation.
TIMEFARME
DELIVERABLES
OUTCOMES
5-10 days
Workload profiling and benchmarking under realistic conditions
Inference optimisation — throughput, latency, batching and memory
Runtime configuration and quantisation strategy
Platform and API layer — routing, failover and enterprise integration
Observability — utilisation, request tracing and operational hygiene
Profiled baseline with documented bottlenecks and performance characteristics
Implemented optimisations validated against your real workload
Runtime configuration tuned to your hardware and use case
Observability in place: utilisation, latency, throughput and request tracing
Knowledge transfer so your team can operate and extend the environment
Configuration documentation for ongoing management