Inference Optimization

Optimize AI inference to reduce latency, cost, and improve scalability and throughput.

Business impact

• Latency — Reduced inference response times improve user experience and system responsiveness
• Cost per inference — Lower operational costs per token reduce total AI deployment expenses
• Throughput — Increased tokens processed per second enable higher workload capacity
• Operational uptime — Improved system reliability ensures continuous AI service availability
• Energy efficiency — Optimized inference reduces power consumption for sustainable AI operations

Data requirements

• GPU performance metrics (Numeric) — Used to monitor and optimize hardware utilization during inference
• Model output logs (Text) — Analyze inference results to detect quality drift and optimize decoding
• System telemetry data (Numeric) — Collect operational data for capacity planning and fault detection
• Token usage statistics (Numeric) — Track token consumption to optimize cost and throughput

AI methods and techniques

• Predictive AI — Forecast workload demands and adjust inference resources proactively
• Agentic AI — Automate inference routing and dynamic resource allocation for efficiency
• Symbolic AI — Apply rule-based optimizations for decoding and quantization processes

AI models and model families

GPT-4o, Claude, vLLM, NVIDIA TensorRT-LLM

Industries

Real-world evidence

16 documented case studies on record.

Companies using this: AMD, Alibaba, Cognition, DeepSeek, Inferact, LangChain, Logic Tronix, Mistral AI, Peking University, Perplexity, Radix Ark, Red Hat, Snowflake, Tensor Mesh, Thoughtworks and 1 more.