# Inference Metrics Framework: TTFT TPS and Memory Profiling > Source-anchored research for Inference Metrics Framework: TTFT TPS and Memory Profiling. Run status: COMPLETED. Parent: [Local Model Performance Evaluation: Ollama, MLX, and Hardware Architectures](https://llms-explorer.com/tree/local-model-performance-evaluation/) · 1 facets · 4 facts · page: https://llms-explorer.com/tree/inference-metrics-framework-ttft-tps-and-memory-profiling/ ## Facts and statements - Time to First Token (TTFT) is compute-bound, determined by prompt batch parallel FLOPs on GPU compute cores, whereas auto-regressive token generation (TPS) is memory bandwidth-bound. (high confidence) — [source](https://github.com/vllm-project/vllm) - Measuring peak VRAM consumption requires tracking resident set size (RSS), active Metal wired memory allocations, and discrete GPU allocated VRAM during maximum prompt length ingestion. (high confidence) — [source](https://github.com/ml-explore/mlx/blob/main/docs/src/usage/benchmarks.rst) - Reporting mean tokens per second without measuring inter-token latency variance masks stutter caused by periodic garbage collection, dynamic KV cache reallocation, and memory compaction. (high confidence) — [source](https://github.com/vllm-project/vllm) - Automated evaluation pipelines must record thermal throttling states and host bus bandwidth saturation alongside inference latency to ensure benchmark reproducibility. (high confidence) — [source](https://github.com/vllm-project/vllm)