AI INFRASTRUCTURE & LLM INFERENCE
Choose the right mix of model APIs, dedicated inference and cloud infrastructure for your real traffic. Balance response time, model quality and operating cost before committing to more hardware.
Share your goals. We’ll help define the next project.
WHEN THIS WORK HELPS
For engineering teams whose model traffic, latency or data handling needs justify a fresh serving decision. The actual constraints, legal requirements and operations team determine the scope.
MAKE COST PART OF THE DESIGN
Respondents to McKinsey’s 2026 AI survey said operating costs were constraining AI use in their organizations.
Source: McKinsey, August 2026. Survey responses describe organizations’ experience, not projected savings for your deployment.
SCOPE AND OUTPUTS
We agree on access, deliverables and checks for your environment.
Calculate VRAM requirements, KV-cache sizing, concurrency thresholds and evaluate quantization trade-offs (FP8, AWQ) for your specific latency targets.
Deliverable: Hardware specification, throughput estimates and total-cost-of-ownership model.
Configure vLLM or TensorRT-LLM with PagedAttention, continuous batching, chunked prefill and speculative decoding on dedicated cloud or bare-metal GPUs.
Deliverable: Production-ready container images, deployment manifests and configuration presets.
Set up intelligent inference load balancing, request queuing and auto-scaling based on real GPU metrics rather than raw HTTP connection counts.
Deliverable: Kubernetes/Docker orchestration with health checks and graceful drain policies.
Monitor tokens-per-second, TTFT, inter-token latency and memory saturation in Prometheus/Grafana, separating operational costs from model experimentation.
Deliverable: Grafana dashboards, alert rules and structured billing attribution.
HOW THE WORK PROGRESSES
Profile prompt lengths, generation volumes, concurrency patterns and data sensitivity to establish the right model size and hardware tier.
Run realistic load tests comparing continuous batching engines and quantization formats against your baseline SLA.
Provision dedicated GPU instances inside your private VPC with strict firewall rules, private endpoints and zero external logging.
Deliver comprehensive operational guides, upgrade procedures, rollback strategies and monitoring runbooks to your infrastructure team.
BEFORE WE START
Price, timing, access and support follow the agreed project scope.
There is no universal token threshold. Compare API bills with hardware, idle capacity, engineering time, reliability and model quality under your own workload.
We shortlist candidates after checking task fit, license, memory needs and evaluation results. A model name alone does not establish production suitability.
We review the infrastructure you already use, region, access and support requirements before selecting a provider or deployment target.
We map each path where prompts or outputs can be logged, cached, backed up or sent externally. Retention settings and deletion behavior need verification in the chosen architecture.
Someone must own model upgrades, capacity, incidents and security after handover. The operating plan should name that owner and its support arrangement.
START WITH THE CONTEXT
Primary guidance to review for your implementation:
START WITH YOUR QUESTION
Tell us what you want to improve. We’ll review your goals and discuss the next step.
“We use a model API and want to compare its cost and latency with a dedicated option under our actual traffic pattern.”
What happens after you send it?We review your description and reply with questions about your project. No calendar booking or newsletter signup.
No technical brief needed to start.