R&D & Benchmarks11 min readOctober 2026

R&D Benchmark: Quantized Local Models vs Cloud Frontier APIs in Enterprise RAG Pipelines

Empirical stress tests: comparing TTFT latency, cost per million tokens, and retrieval accuracy between self-hosted 4-bit models and cloud APIs under 5k queries/min.

Athyrium AI Labs

Applied Research & Quantization Team

Research Objective: Data Sovereignty Without Latency Penalties

For regulated enterprises across fintech, healthtech, and legal verticals, dispatching unencrypted telemetry to public model APIs represents an untenable compliance vulnerability.

Our R&D Labs designed a rigorous empirical testbed to evaluate whether self-hosted quantized models running on private hardware can match or surpass commercial frontier APIs.

Engineering Insight

Benchmark result: 4-bit AWQ quantized models running on vLLM cut ongoing token costs by 73% while retaining 96.4% of cloud API retrieval fidelity.

Hardware Setup & Stress Testing Architecture

We configured a 2-node private cluster with dual NVIDIA RTX 6000 Ada (48GB VRAM each), orchestrating vLLM with PagedAttention and tensor parallelism.

The private RAG pipeline leveraged PostgreSQL pgvector (HNSW indexing with m=16, ef_construction=64) alongside local BAAI/bge-m3 embeddings.

vLLM Engine Setup for Low-Latency Enterprise RAG

from vllm import LLM, SamplingParams

llm = LLM(
    model="meta-llama/Llama-3.3-70B-Instruct",
    quantization="awq",
    tensor_parallel_size=2,
    max_model_len=8192,
    gpu_memory_utilization=0.92,
    enforce_eager=False,
    trust_remote_code=True
)

sampling_params = SamplingParams(
    temperature=0.1,
    top_p=0.95,
    max_tokens=512
)

Key Engineering Takeaways

Average TTFT (Time To First Token) of 184ms on private hardware vs 420ms on commercial cloud APIs.
Zero data egress beyond enterprise VPC firewall boundaries.
Sustained throughput of 142 tokens/second across concurrent multi-turn tool execution.

Topics & Technologies:#R&D#Benchmark#LLMs Locales#vLLM#pgvector#RAG

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