AI infrastructure
Fig. 00 — Simulated compute fabric · Move to place a workload
From silicon to served tokens.
Ybus AI builds the layer between GPU capacity and production AI — designing compute, deploying large language models and engineering high-performance inference for cloud providers, data centers and enterprises.
Our position
Position
Models are only as capable as the infrastructure that serves them. Ybus AI works where accelerators, networks and software meet — engineering compute and inference as one system, so everything built on top is fast, efficient and ready for scale.
01 — Capabilities
Engineered from the rack to the request.
Four disciplines, practised as one. Each decision — accelerator, fabric, precision, runtime — is made with the others in view.
- Fig. 01 — Rack elevation · utilization01
GPU Infrastructure & Compute
Capacity planning, cluster architecture and sourcing across accelerator generations. We design from the workload outward — memory footprint, interconnect topology, power and cooling envelopes — then match it to the right capacity.
- Cluster architecture & topology
- NVLink, InfiniBand & RoCE fabric planning
- Capacity sourcing across providers
- Density, power & cooling envelopes
- Utilization & total-cost modelling
- Fig. 02 — Model sharding · forward pass02
LLM Deployment & Optimization
Taking open and proprietary models from checkpoint to production. Quantization, parallelism strategy and runtime selection, tuned against your latency, quality and cost targets — and gated by evaluation, not intuition.
- FP8 & INT4 quantization with eval gating
- Tensor, pipeline & expert parallelism
- Runtime selection: vLLM, SGLang, TensorRT-LLM
- Fine-tuned & LoRA adapter serving
- Regression & quality harnesses
- Fig. 03 — Request timeline · prefill, decode03
High-Performance Inference
Serving engineered around the numbers that matter in production: time-to-first-token, throughput per GPU and tail latency under real, bursty traffic. Every millisecond and every gigabyte of HBM accounted for.
- Continuous batching & paged KV cache
- Speculative decoding
- Prefill / decode disaggregation
- Autoscaling & multi-region routing
- Observability: TTFT, TPOT, p99
- Fig. 04 — Supply meets demand04
Infrastructure Partnerships
Built for GPU cloud providers, data center operators and capacity holders: we bring deployment expertise and inference workloads to infrastructure — and infrastructure to the teams that need it.
- Workload placement & demand alignment
- Technical integration & onboarding
- AI-ready facility design review
- Flexible & long-term capacity structures
- Joint solution engineering
02 — The stack
Full-stack, facility to API.
Performance is decided at every layer — and lost at the seams between them. We work across all seven, so nothing is optimized in isolation.
OpenAI-compatible endpoints, streaming, request routing, rate limiting and usage metering — the surface your applications actually touch.
Quantization, parallelism layout, speculative decoding and adapter strategy — chosen per model and verified against quality baselines.
Engine selection and tuning: continuous batching, paged attention, prefix caching and CUDA graph capture, matched to the traffic profile.
Kubernetes and Slurm, gang scheduling, multi-tenant isolation and autoscaling — so capacity is shared without performance being shared away.
NVLink domains, InfiniBand and RoCE fabrics, rail-optimized topologies and topology-aware placement — where distributed inference is won or lost.
Matching accelerator generation, HBM capacity and node configuration to the workload — rather than the workload to whatever is available.
Power envelopes, rack density, liquid versus air cooling and the physical constraints that set the ceiling for everything above.
03 — Sizing
Size a deployment.
A first-order estimate of the accelerator memory a large language model needs to serve — weights plus KV cache — and the GPU footprint that implies. The real answer depends on your traffic. That is where we come in.
- Weights
- 70.6 GB
- KV cache
- 85.9 GB
- KV / token
- 160 KiB
- Min. model shard
- TP2
- Nodes
- 1 · 4/8 GPU
- HBM in use
- 49%
Minimum footprint for a dense, grouped-query-attention transformer with Llama 3.1-class dimensions; KV cache at FP8 unless serving BF16; 10% of each GPU held back for activations and runtime. The model shard holds the weights plus one full-context sequence. Indicative only — excludes replicas, redundancy and traffic shape.
Validate with an engineer04 — Approach
Workload first. Hardware second.
Every engagement follows the same discipline: understand the work, design the system around it, ship it, then keep making it better.
- 01
Assess
We start with the workload: models, traffic shape, latency targets, data constraints and budget. Not with a catalogue.
- 02
Architect
A reference design spanning compute, fabric, runtime and serving — with every trade-off made explicit and quantified.
- 03
Deploy
Capacity secured, clusters stood up, models optimized and brought into production behind stable, observable APIs.
- 04
Optimize
Continuous measurement and tuning of throughput, latency and cost as models, hardware and demand evolve.
05 — Partnerships
Built to partner.
Ybus AI is building a network of infrastructure partners. If you hold capacity, build facilities or run AI workloads at scale, there is a shape of partnership that fits — and we would like to find it with you.
- For
GPU cloud providers
Put capacity to productive use. We bring deployment expertise and inference workloads engineered to run efficiently on your platform.
- Workload placement
- Technical integration
- Inference-ready configurations
- For
Data centers & capacity holders
Turn space, power and accelerators into AI-ready infrastructure, designed for the density, cooling and fabric that modern models demand.
- AI-ready design review
- Density & cooling planning
- A path to utilization
- For
Enterprises & AI teams
Run large models in production without building an infrastructure team from scratch. Private, dedicated or hybrid — engineered to your constraints.
- Private & dedicated deployments
- Cost-performance tuning
- Ongoing optimization
Exploring a partnership? Start with a short note about your capacity or your workload.
Discuss a partnership06 — Contact
Let’s build what intelligence runs on.
What’s it about?
Tell us about the workload, the capacity or the constraint. A few lines is enough to start the conversation.