Private LLM Inference on Consumer Blackwell GPUs

arxiv.org · rohansood15 · 14 hours ago · view on HN · research
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Systematic benchmarking of NVIDIA Blackwell consumer GPUs for LLM inference across quantization formats and workloads, demonstrating cost-effective private deployment for SMEs with 40-200x lower costs than cloud APIs and sub-second latency for most use cases.

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llm-inference gpu-optimization quantization model-deployment privacy performance-benchmarking nvidia-blackwell cost-analysis rag model-serving
Entities
NVIDIA Blackwell RTX 5060 Ti RTX 5070 Ti RTX 5090 Qwen3-8B Gemma3-12B Gemma3-27B GPT-OSS-20B Jonathan Knoop Hendrik Holtmann
[2601.09527] Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs Support arXiv on Cornell Giving Day! We're celebrating 35 years of open science - with YOUR support! Your generosity has helped arXiv thrive for three and a half decades. Give today to help keep science open for ALL for many years to come. Donate! --> Computer Science > Machine Learning arXiv:2601.09527 (cs) [Submitted on 14 Jan 2026] Title: Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs Authors: Jonathan Knoop , Hendrik Holtmann View a PDF of the paper titled Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs, by Jonathan Knoop and 1 other authors View PDF HTML (experimental) Abstract: SMEs increasingly seek alternatives to cloud LLM APIs, which raise data privacy concerns. Dedicated cloud GPU instances offer improved privacy but with limited guarantees and ongoing costs, while professional on-premise hardware (A100, H100) remains prohibitively expensive. We present a systematic evaluation of NVIDIA's Blackwell consumer GPUs (RTX 5060 Ti, 5070 Ti, 5090) for production LLM inference, benchmarking four open-weight models (Qwen3-8B, Gemma3-12B, Gemma3-27B, GPT-OSS-20B) across 79 configurations spanning quantization formats (BF16, W4A16, NVFP4, MXFP4), context lengths (8k-64k), and three workloads: RAG, multi-LoRA agentic serving, and high-concurrency APIs. The RTX 5090 delivers 3.5-4.6x higher throughput than the 5060 Ti with 21x lower latency for RAG, but budget GPUs achieve the highest throughput-per-dollar for API workloads with sub-second latency. NVFP4 quantization provides 1.6x throughput over BF16 with 41% energy reduction and only 2-4% quality loss. Self-hosted inference costs $0.001-0.04 per million tokens (electricity only), which is 40-200x cheaper than budget-tier cloud APIs, with hardware breaking even in under four months at moderate volume (30M tokens/day). Our results show that consumer GPUs can reliably replace cloud inference for most SME workloads, except latency-critical long-context RAG, where high-end GPUs remain essential. We provide deployment guidance and release all benchmark data for reproducible SME-scale deployments. Comments: 15 pages, 18 tables, 7 figures. Includes link to GitHub repository and Docker image for reproducibility Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Performance (cs.PF) Cite as: arXiv:2601.09527 [cs.LG] (or arXiv:2601.09527v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2601.09527 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jonathan Knoop [ view email ] [v1] Wed, 14 Jan 2026 14:49:07 UTC (30 KB) Full-text links: Access Paper: View a PDF of the paper titled Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs, by Jonathan Knoop and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-01 Change to browse by: cs cs.AI cs.PF References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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