Production-grade HPC and AI/ML solutions optimized for true performance. From model inference optimization and DevOps automation to applied AI development and inference-optimized model creation.
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Innovative tools and platforms designed to solve real-world problems.
Field reports for operators who care about margin, reliability, and the real economics behind production AI systems.
New Report
A data-first look at token price collapse, hyperscaler capex, provider margins, hidden AI costs, and whether cheap token pricing can last.
Includes the 99.7% token cost drop, $725B AI capex signal, margin risk, and builder guidance for 2026.
Live Now
A 4-part breakdown of the cost paradox: 99.7% token price drop, 3× bill growth, and 72% spend hiding outside inference.
Includes benchmark numbers, hidden-cost anatomy, and an operator-ready optimization sequence.
Interactive tools that give you answers in minutes, not meetings.
Size your on-prem GPU cluster, compare against cloud API costs, and find your break-even point — all in one tool.
$ estimate --model llama-3-70b
Hardware: 2× NVIDIA A100 80GB
On-prem cost: $4,200/mo
Cloud equiv: $18,600/mo
Break-even: 4.2 months
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Right-size your GPU cluster, networking, and storage for any AI workload — training or inference.
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Analyze prompt token usage across providers and find the cheapest path to production-quality output.
Deep dives into model optimization, HPC, MLOps, DevOps, and production-grade AI/ML engineering.
We benchmarked Llama 3 70B across FP16, INT8 quantization, and KV-cache pruning on H100 and A100 GPUs. The results: 42% lower cost per million tokens and 2.3x throughput — without quality loss you'd notice in production.
We built the same embedding + rerank gateway in Python, Rust (ONNX), and a split architecture — then benchmarked all three on GCP. Rust hit 28% more RPS with 67% less memory. Same model, same API.
Compress Orpheus-3B TTS to half its size with no quality loss. Step-by-step guide using self-knowledge distillation, Unsloth, SNAC tokenization, and LoRA — with full code examples you can run today.
Production inference work should show up in unit economics, not just benchmark charts.
Llama 3 70B inference audit
A client running unoptimized Llama 3 70B at roughly 200M tokens per month was carrying low utilization and duplicated GPU spend. After INT8 quantization, KV-cache pruning, and batching tuning, they consolidated from 4 GPUs to 2 while keeping quality within production noise.
42%
cost reduction per million tokens
2.3x
throughput improvement
$47K -> $28K
monthly bill after optimization
Best fit for CTOs, founders, and ML platform leads with production LLM traffic, rising inference spend, or agentic workflows that are hard to forecast.
$20K-$200K/month AI spend, high-volume tokens, RAG, or self-hosting decisions
Andhra Pradesh, India
Applied AI development involves the practical implementation of artificial intelligence technologies to solve real-world business problems. At NavyaAI, we specialize in building production-grade AI systems—including LLMs, agents, and conventional ML models—that deliver measurable ROI and are governed, explainable, and reliable from day one.
Model inference optimization focuses on improving the speed, efficiency, and resource utilization of AI models during deployment. This includes techniques like quantization, pruning, knowledge distillation, and using specialized hardware or inference frameworks. Our optimization services reduce memory footprint, lower computation complexity, and decrease inference latency while maintaining model performance.
AI ML consulting costs vary based on project scope, complexity, and duration. At NavyaAI, we offer flexible engagement models tailored to your needs. We provide transparent pricing and work with businesses of all sizes. Contact us for a customized quote based on your specific requirements.
We work with a wide range of technologies including Rust, Python, Golang, Mojo, and C. Our expertise spans HPC solutions, MLOps, DevOps automation, and production-grade AI/ML systems. We choose the best technology stack based on your performance requirements and infrastructure constraints.
Yes, NavyaAI specializes in end-to-end AI application development. From initial strategy and model design to deployment, optimization, and ongoing maintenance, we handle the complete lifecycle of AI applications. Our services include model inference optimization, DevOps automation, and production-grade system development.
