Deterministic Evaluation and FP8 Quantization for Enterprise LLMs
How to run 70B parameter models with sub-250ms time-to-first-token while maintaining strict safety and zero hallucination drift.
## The Cost and Latency Bottleneck
Deploying raw unquantized 70B parameter weights on 8x H100 clusters incurs unsustainable operational costs. For latency-critical customer workflows, time-to-first-token must remain strictly under 300ms.
FP8 Quantization with Minimal Perplexity Degradation
By utilizing post-training quantization (PTQ) techniques with smooth quant calibration, we compressed weight tensors into FP8 representations with less than 0.8% perplexity penalty.
from vllm import LLM, SamplingParamsllm = LLM(
model="sylvaxo/frontier-instruct-70b",
quantization="fp8",
gpu_memory_utilization=0.92,
max_model_len=8192
)Automated Regression Guardrails
Every checkpoint runs against our 500-question deterministic regression suite. Outputs are scored on: - Schema adherence (100% JSON valid) - Factual grounding against gold enterprise documents - Absence of toxic or biased tokens