RAG Evaluation Frameworks & LLM Serving Metrics: TTFT, Throughput, Latency & VRAM Sizing
RAGAS triad metrics alongside operational serving parameters: Time To First Token, decode speed, throughput trade-offs, and VRAM memory math.
⚡ Executive Summary
A practical operational guide covering RAG metrics (Context Precision, Recall, Groundedness) and physical serving metrics (TTFT, VRAM sizing, Throughput).
Key Takeaways
- ✓RAG-Specific Metrics (RAGAS / TruLens) — covered in depth with practical examples, formulas, and code.
- ✓Deployment & Inference Serving Metrics — covered in depth with practical examples, formulas, and code.
RAG Evaluation & Production Serving Metrics
#1. RAG-Specific Metrics (RAGAS / TruLens)
| Metric | Evaluates | Failure mode caught |
|---|---|---|
| Context Precision | Retriever | Noise / irrelevant retrieved chunks |
| Context Recall | Retriever | Missing necessary information |
| Groundedness | Generator | Model hallucinating beyond context |
| Answer Relevancy | Generator | Answering a different question |
from ragas import evaluate
from ragas.metrics import context_precision, context_recall, faithfulness, answer_relevancy
results = evaluate(dataset, metrics=[context_precision, context_recall, faithfulness, answer_relevancy])#2. Deployment & Inference Serving Metrics
2.1 TTFT — Time To First Token
Latency from request start to token #1 output. Measures user-perceived responsiveness.
2.2 Tokens/sec & Latency
Rate of output token generation during decode phase. p50/p90/p99 latency percentiles.
2.3 Throughput
Total tokens processed per unit time across concurrent requests:
2.4 VRAM Sizing Formula
GPU memory footprint for model weights:
Yashika P
Founder @ AscendiaEdu & Lead DevOps Engineer
Yashika P is part of the Junglans Solutions engineering team, specializing in llm evaluation & benchmarking. Junglans builds a 20-product ecosystem of local-first enterprise software — AI developer tools, encrypted communication, and data infrastructure with zero cloud telemetry.
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