Inference Optimization

Inference telemetry and benchmarks optimize throughput, latency, cost, and power across diverse AI hardware platforms.

Business impact

• Inference throughput — Increased throughput enables faster processing of AI inference workloads
• Inference interactivity — Lower latency improves responsiveness and user experience during inference
• Inference cost — Optimized resource use reduces cost per token and overall inference expenses
• Power consumption — Efficient hardware-software integration lowers energy usage during inference

Data requirements

• Inference logs and telemetry (Numeric) — Used to benchmark and optimize inference throughput and latency
• Hardware performance counters (Numeric) — Provide metrics on power consumption and GPU utilization for optimization
• Model weights and architecture metadata (Structured) — Inform quantization and pruning strategies to reduce computational load
• Token sequences and cache states (Text) — Enable scheduling and caching optimizations during inference

AI methods and techniques

• Predictive AI — Forecast workload patterns to optimize resource allocation and scheduling
• Agentic AI — Manage dynamic inference workflows and adapt to varying input-output token lengths
• Symbolic AI — Apply rule-based scheduling and cache management for efficient inference execution

AI models and model families

GPT-4o, Claude, Llama, DeepSeek V4-Pro

Industries

Real-world evidence

13 documented case studies on record.

Companies using this: AMD, Alibaba, Anthropic, BentoML, ByteDance, Cerebras, DeepSeek, LogicTronix, OpenAI, Perplexity, Salesforce, Semi Analysis, Xiaomi.