GetGPUFast.ai

GPU & AI Infrastructure Strategy
Powered by Team Rays LLC

vendor-neutral • architecture-first • cost-optimized

Turn GPU chaos into a clean, scalable AI compute plan.

We help AI teams and quantitative researchers pick the right GPUs, the right platform, and the right architecture across cloud and hybrid environments—then deliver an implementation roadmap your engineers can execute.

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Typical outcomes: fewer failed runs, faster iteration, clearer scaling strategy, and lower monthly GPU spend.

GPU sizing Cloud / hybrid LLM/RAG infra Quant research compute Security posture

What you get

GPU sizing & selection: match workload → GPU class, VRAM, interconnect, storage
Architecture blueprint: diagrams for training, inference, RAG, or research clusters
Cost model: clear assumptions + monthly estimates + scale scenarios
Performance plan: throughput/latency targets, batching, caching, observability
Security checklist: segmentation, IAM/key mgmt, logging, baseline controls

Best fit for

AI startups scaling inference and RAG systems
Teams training models or running multi-GPU experiments
Quant researchers needing repeatable, faster compute pipelines

GPU Fit Check

Quick recommendation to stop guesswork and pick the right compute direction.

30–45 min intake
GPU + platform recommendation
1-page action plan

Architecture Assessment

Reference architecture + cost model + implementation steps.

Architecture diagram(s)
Cost model + scaling plan
Ops + security checklist

Implementation Advisory

Hands-on weekly guidance while your team builds, tunes, and ships.

Weekly architecture office hours
Performance & cost tuning
Rollout & reliability support

Pricing

Clear, practical packages designed to create measurable outcomes fast.

Starter: GPU Fit Check

Best if you need a fast decision on GPU type + platform direction.

Workload intake + constraints
GPU recommendation (VRAM, batch sizing guidance)
Platform direction (cloud/hybrid)

$750

one-time

Pro: Architecture Assessment

Best if you need a real blueprint with cost + scaling assumptions.

Architecture diagram + components
Cost model + scale scenarios
Security + ops checklist

$2,500

one-time

Elite: Implementation Advisory

Best if you want weekly guidance to ship faster and avoid costly mistakes.

Weekly sessions + async Q&A
Perf/cost tuning plan
Rollout + reliability support

$3,500

per month

Want something custom (multi-team rollout, hybrid security requirements, or procurement constraints)? Use the contact form below.

Quant Compute Angle

For quantitative research teams, GPU compute is often the bottleneck. We help you build repeatable pipelines that finish faster and cost less.

Research Acceleration

Speed up experimentation loops for feature generation, model training, and hyperparameter runs.

Batching + job orchestration approach
Reproducible environments
Compute cost controls

Backtesting at Scale

Architecture patterns for distributed workloads and data-heavy simulations.

Storage + caching strategy
Parallelization plan
Observability + reliability

Low-Latency Inference

Optimize inference throughput and latency for signal generation systems.

Model serving pattern
Batching/caching tradeoffs
Cost-per-inference tuning

Contact & Lead Capture

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