Engineered Precision, Delivered
Sovereign AI inference for real work. Run code development and agentic workflows on infrastructure built for control, measurable performance and room to grow.
New: the Argyll deployment planner
Plan your AI deployment in 3D
Enter your room, power and cooling, choose how many racks you need, and walk through the result in 3D. The planner checks that it fits, that the power and cooling are enough, and that the floor can take the weight.
When you are happy with it, send the design to Argyll and we will work through it and price it with you.
Free to use in your browser. A planning guide, not a design sign-off.
Why inference architecture matters
Plays from YouTube when you press play.
A code-building or agentic workflow can make many model requests to complete one task. Response time, throughput and energy use matter at every step. This SambaNova video explains how its dataflow architecture is designed to keep inference moving through those repeated requests.
What comes next for inference
SN50 is planned from Q2 2027, offered as a managed service or for outright sale. It shows how Argyll is building its inference offering around the needs of real workloads.
SN 50 available from Q2 2027
SN50 is SambaNova’s fifth-generation Reconfigurable Dataflow Unit, designed for large AI models and demanding inference workloads.
Fast token generation: Designed for the decode stage of inference, when a model generates its response.
Agentic workloads: Built for coding agents and other applications that make repeated, complex requests.
Continuous dataflow: Chains operations together to reduce repeated movement between compute and memory.
Support for concurrent users: Designed to sustain throughput as multiple users and workloads run.
Three-tier memory: Combines on-chip SRAM, high-bandwidth memory and DDR5 to keep frequently used data close to compute.
RDU specifications: Each SN50 provides 432 MB of on-chip SRAM, 64 GB of HBM2E and support for up to 2 TB of DDR5.
Rack configuration, deployment options and operating power will depend on the SN50 system offered as managed service or outright sale.
Choosing compute for the workload
Different AI tasks place different demands on infrastructure. SambaNova’s video explores how specialised inference can work alongside other forms of compute. For customers, the goal is practical: choose an architecture that fits the workload, keeps data under appropriate control and can be measured as demand grows.
Plays from YouTube when you press play.
Plan your deployment
Tell us what your users need to do, where your data must remain and how you want the service operated. We can help you work through the right deployment approach for your organisation.
Wish to look at planning data?