Deploying 70B LLMs On-Premises: Enterprise Hardware Selection and Architecture Guide

Introduction: Why Enterprises Are Considering On-Premises LLM Deployment

Recently, a technical lead at an enterprise posted a highly representative question on Reddit: their company was planning to purchase servers to run open-source large language models with over 70B parameters on-premises, serving roughly 200 internal users — and they wanted to know how to approach hardware selection.

The question sounds straightforward, but it touches on multiple dimensions: inference engines, VRAM capacity, concurrent throughput, and cost control. With the rise of high-quality open-source models like Llama 3, Qwen, and DeepSeek, more and more enterprises are choosing to deploy LLMs in their own data centers — rather than relying on cloud APIs — driven by data privacy, compliance requirements, and long-term cost considerations. This article systematically breaks down the hardware options for deploying 70B-class models on-premises based on this real-world use case.

Clarifying Requirements: What Is the Real Concurrency for 200 Users?

Before discussing hardware, it's essential to clarify a key concept: 200 users ≠ 200 concurrent requests.

The Difference Between User Count and Concurrency

Among 200 internal employees, the vast majority will not be sending requests to the model simultaneously at any given moment. Based on typical enterprise application patterns, the actual number of simultaneously active concurrent requests is usually only 5%–15% of registered users. That means 200 users translates to a peak concurrency of roughly 10–30 simultaneous requests.

This distinction is critical for hardware selection. Designing for 200 fully concurrent requests would multiply hardware costs several times over, while designing for realistic concurrency can significantly reduce investment. Enterprises should first conduct the following assessments:

• Peak concurrency estimation: Observe usage patterns to determine the upper limit of simultaneous requests.
• Response latency requirements: Customer service Q&A applications are sensitive to time-to-first-token, while document summarization tasks can tolerate longer queuing times.
• Context length: Long-document processing (e.g., 32K/128K context windows) substantially increases VRAM consumption.

The Core Bottleneck: Estimating VRAM Requirements

Running a 70B parameter model is fundamentally constrained by GPU VRAM. The memory required for model weights can be roughly estimated as follows:

• FP16/BF16 precision: ~140GB VRAM (70B × 2 bytes)
• INT8 quantization: ~70GB VRAM
• INT4 quantization: ~35–40GB VRAM

Beyond model weights, you also need to reserve space for the KV Cache (key-value cache), which grows linearly with concurrency and context length. In high-concurrency scenarios, the KV Cache can actually exceed the memory footprint of the model weights themselves.

Trade-offs in Quantization Precision

Quantization can significantly reduce VRAM requirements but introduces some accuracy loss. For enterprise applications, INT8 quantization is typically within an acceptable range, while INT4 (e.g., GPTQ, AWQ) is suitable for cost-sensitive scenarios that can tolerate slight quality degradation. Before production deployment, it's recommended to run A/B evaluations on different quantization levels using real business data.

Hardware Option Comparison: GPU Servers, Mac Studio, and DGX

The original post highlighted two widely discussed options: Apple Mac Studio and the Nvidia DGX Spark. Here's a comparative analysis alongside other mainstream choices.

Option 1: Nvidia Professional-Grade GPU Servers

For enterprise production environments serving multiple users, Nvidia data center GPUs remain the most mature choice with the most complete ecosystem:

• Single H100 80GB / A100 80GB: A single card can run a 70B model with INT4 quantization, but concurrent capacity is limited.
• Dual-card / Quad-card configurations: Using Tensor Parallelism to distribute model weights, 2×H100 can smoothly run a 70B model at FP16 precision with support for higher concurrency.
• Inference engines: Paired with high-performance frameworks like vLLM, TensorRT-LLM, or SGLang, continuous batching can be enabled to dramatically improve throughput.

For a scenario with 200 users and 10–30 concurrent requests, 2 professional cards with 80GB VRAM each (A100/H100) is generally a well-balanced starting point in terms of cost-effectiveness.

Option 2: Apple Mac Studio (Unified Memory Architecture)

Apple Silicon's Unified Memory architecture has made Mac Studio a popular choice for individuals and small teams running large models. The top-spec M2/M3 Ultra can be configured with up to 192GB of unified memory — enough to load a 70B or even larger model.

Advantages: Low power consumption, quiet operation, simple single-machine deployment, and relatively manageable acquisition costs.

Limitations: Its memory bandwidth and parallel inference throughput are far inferior to Nvidia data center GPUs. Mac Studio is an excellent choice for single-user or very low-concurrency exploratory use, but it struggles to handle enterprise-grade production loads for 200 users.

Option 3: DGX Spark and Integrated Appliances

Nvidia's DGX series and the newly released DGX Spark are turnkey AI appliances with a complete software stack pre-installed, suitable for enterprises lacking dedicated operations teams that want to go live quickly. The trade-off is higher acquisition cost and less flexibility compared to building your own server.

Deployment Architecture Recommendations: From Getting Started to Scaling

For the scenario described in the original post, here is a practical deployment path:

Initial Phase

1. Hardware selection: 2×A100 80GB or 2×H100 server running a 70B model with INT8 quantization.
2. Inference layer: Deploy vLLM as the inference service, enabling continuous batching and PagedAttention for optimized VRAM utilization.
3. Access layer: Manage requests uniformly through an OpenAI-compatible API gateway for monitoring and rate limiting.

Scaling Phase

As user numbers and concurrency grow, horizontal scaling can be achieved by adding GPU nodes with load balancing and request queuing. A robust monitoring system should also be established to track key metrics such as GPU utilization, VRAM usage, time-to-first-token, and throughput.

Cost Analysis: On-Premises vs. Cloud API

On-premises deployment requires significant upfront investment — a dual-H100 server can cost tens of thousands of dollars in hardware alone, with additional considerations for data center space, power, cooling, and operations staff. However, for enterprises with sensitive data and sustained high-frequency usage, on-premises deployment often becomes more economical than ongoing cloud API calls within 12–24 months, while also satisfying compliance requirements that data must remain on-network.

Conversely, if usage frequency is uncertain or you're in a validation phase, it's advisable to first validate your use case with cloud GPU rental or API services, then decide whether to build on-premises based on actual load data.

Conclusion

Deploying a 70B LLM on-premises is far more than just "buying a GPU" — it's a systems engineering challenge that requires holistic consideration of concurrency scale, VRAM budget, quantization strategy, inference framework, and long-term costs. For enterprises serving 200 users, dual 80GB professional GPUs + a vLLM inference stack is a solid starting point. Mac Studio is better suited for exploratory validation, while DGX appliances target teams prioritizing rapid deployment. The final solution should be grounded in real business concurrency data — not raw user counts — to avoid over-engineering the hardware stack.