Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

TL;DR

This work systematically benchmarks NVIDIA Blackwell consumer GPUs (RTX 5060 Ti, 5070 Ti, 5090) for production LLM inference on open-weight models (Qwen3-8B, Gemma3-12B/27B, GPT-OSS-20B) across 79 configurations and three SME-focused workloads (RAG, multi-LoRA agentic serving, high-concurrency APIs). It demonstrates that NVFP4 quantization offers the best throughput-per-watt, enabling 1.6× throughput gains with 41% energy savings over BF16 and acceptable quality loss, and shows self-hosted inference costs ($0.001–$0.04/MTok for API workloads) can be 40–200× cheaper than budget cloud APIs, breaking even within months at moderate usage. The findings identify context length as the primary cost driver, with 8k contexts offering substantial economy and 32k+ contexts requiring dual-GPU setups for acceptable latency, while dual-GPU budget configurations provide strong latency reductions for agentic workloads. The paper provides practical deployment guidance, including GPU/model/quantization choices by workload, break-even analyses, and reproducible benchmark data to support SME-scale local deployment decisions. Overall, the study affirms that consumer GPUs can reliably replace cloud inference for most SME workloads, with RTX 5090 necessary mainly for latency-sensitive long-context RAG and very high-throughput scenarios. The release of benchmark data, Docker images, and scripts facilitates reproducible, SME-scale deployment planning.

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.

Figures (7)

• Figure 1: GPU performance hierarchy: throughput (TPS) for Qwen3-8B NVFP4 across workloads. The RTX 5090 delivers 3.5--4$\times$ higher throughput than the 5060 Ti.
• Figure 2: Latency vs. throughput trade-off (Qwen3-8B NVFP4, RAG workloads). Lower-right is better. Only RTX 5090 configurations achieve sub-second TTFT with high throughput.
• Figure 3: Context length impact on throughput (Qwen3-8B NVFP4). Doubling context approximately halves throughput.
• Figure 4: Quantization format comparison (Qwen3-8B, RTX 5090 1x). Higher is better. NVFP4 outperforms W4A16 on all metrics.
• Figure 5: Agentic workload scaling (Qwen3-8B NVFP4, 3 LoRA adapters). RTX 5090 and 5070 Ti scale with concurrency; RTX 5060 Ti saturates at c32.