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Viability and Performance of a Private LLM Server for SMBs: A Benchmark Analysis of Qwen3-30B on Consumer-Grade Hardware

Alex Khalil, Guillaume Heilles, Maria Parraga, Simon Heilles

TL;DR

The paper investigates the viability of private, on-prem LLM inference for SMBs by deploying a quantized Qwen3-30B-A3B model on a consumer-grade RTX 5090 and benchmarking both model quality and server performance. The approach combines mid-bit quantization (Q6_K_XL) with a streamlined inference stack (llama.cpp) to evaluate cloud-parity for light multi-user workloads, using AIME and MMLU for quality and latency/throughput metrics for system performance. Results show competitive model quality relative to cloud baselines for single-user scenarios, while concurrency introduces pronounced efficiency losses driven by prefill costs and serialization in the serving stack; improvements in runtime scheduling and multi-GPU scaling are identified as key enablers for broader SMB adoption. The findings offer a practical pathway for SMBs to deploy private LLMs at manageable costs and privacy assurances, while outlining concrete directions for future work to close the gap with cloud services under higher concurrent demand.

Abstract

The proliferation of Large Language Models (LLMs) has been accompanied by a reliance on cloud-based, proprietary systems, raising significant concerns regarding data privacy, operational sovereignty, and escalating costs. This paper investigates the feasibility of deploying a high-performance, private LLM inference server at a cost accessible to Small and Medium Businesses (SMBs). We present a comprehensive benchmarking analysis of a locally hosted, quantized 30-billion parameter Mixture-of-Experts (MoE) model based on Qwen3, running on a consumer-grade server equipped with a next-generation NVIDIA GPU. Unlike cloud-based offerings, which are expensive and complex to integrate, our approach provides an affordable and private solution for SMBs. We evaluate two dimensions: the model's intrinsic capabilities and the server's performance under load. Model performance is benchmarked against academic and industry standards to quantify reasoning and knowledge relative to cloud services. Concurrently, we measure server efficiency through latency, tokens per second, and time to first token, analyzing scalability under increasing concurrent users. Our findings demonstrate that a carefully configured on-premises setup with emerging consumer hardware and a quantized open-source model can achieve performance comparable to cloud-based services, offering SMBs a viable pathway to deploy powerful LLMs without prohibitive costs or privacy compromises.

Viability and Performance of a Private LLM Server for SMBs: A Benchmark Analysis of Qwen3-30B on Consumer-Grade Hardware

TL;DR

The paper investigates the viability of private, on-prem LLM inference for SMBs by deploying a quantized Qwen3-30B-A3B model on a consumer-grade RTX 5090 and benchmarking both model quality and server performance. The approach combines mid-bit quantization (Q6_K_XL) with a streamlined inference stack (llama.cpp) to evaluate cloud-parity for light multi-user workloads, using AIME and MMLU for quality and latency/throughput metrics for system performance. Results show competitive model quality relative to cloud baselines for single-user scenarios, while concurrency introduces pronounced efficiency losses driven by prefill costs and serialization in the serving stack; improvements in runtime scheduling and multi-GPU scaling are identified as key enablers for broader SMB adoption. The findings offer a practical pathway for SMBs to deploy private LLMs at manageable costs and privacy assurances, while outlining concrete directions for future work to close the gap with cloud services under higher concurrent demand.

Abstract

The proliferation of Large Language Models (LLMs) has been accompanied by a reliance on cloud-based, proprietary systems, raising significant concerns regarding data privacy, operational sovereignty, and escalating costs. This paper investigates the feasibility of deploying a high-performance, private LLM inference server at a cost accessible to Small and Medium Businesses (SMBs). We present a comprehensive benchmarking analysis of a locally hosted, quantized 30-billion parameter Mixture-of-Experts (MoE) model based on Qwen3, running on a consumer-grade server equipped with a next-generation NVIDIA GPU. Unlike cloud-based offerings, which are expensive and complex to integrate, our approach provides an affordable and private solution for SMBs. We evaluate two dimensions: the model's intrinsic capabilities and the server's performance under load. Model performance is benchmarked against academic and industry standards to quantify reasoning and knowledge relative to cloud services. Concurrently, we measure server efficiency through latency, tokens per second, and time to first token, analyzing scalability under increasing concurrent users. Our findings demonstrate that a carefully configured on-premises setup with emerging consumer hardware and a quantized open-source model can achieve performance comparable to cloud-based services, offering SMBs a viable pathway to deploy powerful LLMs without prohibitive costs or privacy compromises.
Paper Structure (27 sections, 5 figures, 1 table)

This paper contains 27 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and related work
  3. Quantization for consumer hardware
  4. On-Device and sovereign deployment
  5. Benchmarks and comparative studies
  6. Methodology
  7. Hardware setup
  8. Software environment
  9. Model configuration
  10. Evaluation benchmarks
  11. Quality Benchmarks
  12. System benchmarks
  13. Workload generation
  14. Baselines
  15. Results
  16. ...and 12 more sections

Figures (5)

  • Figure 1: Overall comparison of AIME model accuracy for 2024 and 2025.
  • Figure 2: MMLU — model accuracy compared with cloud-based baselines
  • Figure 3: Spearman Correlation Matrix
  • Figure 4: System performance as a function of token count. Top row: End-to-end latency exhibits super-linear growth with Total_Tokens, driven primarily by the number of output tokens. Bottom row: System completion rate (throughput) degrades sharply with token count, with input token length (prefill) emerging as the key bottleneck for overall throughput.
  • Figure 5: System performance under increasing concurrent loads