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AI Inference Server for Production: Configuration Guide
A production AI inference server in 2026 runs a trained LLM and serves predictions to end users with low latency and high availability. The configuration depends on three variables: the model size you are serving, the number of concurrent users, and the latency SLA your application requires. A single RTX PRO 6000 Blackwell (96 GB) serves 70B models for small teams. A 4-GPU 2U EPYC server handles medium-scale production. An 8-GPU 4U server serves frontier models or high-concurrency deployments.
This guide walks through GPU sizing, inference framework selection, CPU and memory configuration, form factor choice, and the cost comparison with API-based inference. Every configuration is available from VRLA Tech GPU servers.
GPU sizing by model and concurrency
| Model | Precision | VRAM (weights + KV cache) | GPU Configuration | Approximate Concurrent Users |
|---|---|---|---|---|
| 7B–13B | FP8 / Q4 | 8–20 GB | 1× RTX PRO 4000 Blackwell (24 GB) | 50–100+ |
| 30B | FP8 | 30–40 GB | 1× RTX PRO 6000 Blackwell (96 GB) | 30–60 |
| 70B | FP8 | 70–90 GB | 1× RTX PRO 6000 Blackwell (96 GB) | 10–30 |
| 70B | FP16 | 140–180 GB | 2× RTX PRO 6000 Blackwell (192 GB) | 20–50 |
| 405B | FP8 | 405–500 GB | 6–8× RTX PRO 6000 Blackwell | 10–30 |
| 405B | FP16 | 810+ GB | H200 or B200 SXM cluster | Varies |
Concurrent user estimates assume vLLM with continuous batching, average prompt length of 500 tokens, average generation of 200 tokens, and interactive latency (under 2 seconds time-to-first-token). Your actual numbers will vary with workload profile — VRLA Tech engineers model these during the quoting process.
Inference framework selection
The inference framework determines how efficiently the server uses GPU resources. The three production-grade options in 2026 are vLLM (the default for most deployments — open-source, continuous batching, tensor parallelism, PagedAttention for KV cache management), TensorRT-LLM with NVIDIA Triton (maximum throughput on NVIDIA hardware, best for latency-critical applications with predictable workloads), and SGLang (strong for structured generation, function calling, and agentic workflows). Ollama and LM Studio are development tools, not production inference frameworks — they lack continuous batching, multi-user serving, and production monitoring.
VRLA Tech pre-installs and validates your chosen inference framework on every GPU server before shipping. The full stack — CUDA, cuDNN, NCCL for multi-GPU communication, PyTorch, and the inference framework — is tested under sustained load during the 48–72 hour burn-in process.
Server platform and form factor
All VRLA Tech inference servers use AMD EPYC 9005 processors. EPYC provides the PCIe Gen 5 lane count (128 lanes per socket) and memory bandwidth (12 DDR5 channels per socket) that multi-GPU inference requires. The CPU handles request routing, tokenization, and KV cache management — workloads where core count and memory bandwidth matter more than single-thread performance.
For form factor: a 2U server with 4 RTX PRO 6000 Blackwell Server Edition GPUs is the recommended starting point for most production inference deployments. It delivers 384 GB of VRAM in minimum rack space. Choose a 4U server when you need 8 GPUs per node, or when sustained 24/7 operation at maximum throughput demands the extra cooling headroom. See the form factor comparison guide for the detailed trade-offs.
Redundant hot-swap PSUs, IPMI remote management, and enterprise NVMe storage are standard on every VRLA Tech inference server. ConnectX-7 or ConnectX-8 network adapters are available for 100GbE or InfiniBand connectivity in multi-node deployments.
On-premise inference vs API and cloud GPU
For teams currently using OpenAI, Anthropic, or Google APIs for production inference, the cost comparison with on-premise hardware is decisive at sustained utilization. A 4-GPU EPYC inference server running a 70B open-source model via vLLM can serve the same workload at a fraction of the per-token cost of commercial APIs — with no rate limits, no egress fees, no data leaving your facility, and no vendor dependency on model availability or pricing changes.
For teams renting cloud GPU instances (AWS, GCP, Lambda), on-premise hardware typically breaks even in 4–8 weeks at 8+ hours per day utilization. After break-even, compute is effectively free. Use the VRLA Tech AI ROI Calculator to model your specific scenario.
Cloud inference remains the better choice for burst workloads, early prototyping, and scaling beyond what a single on-premise node can deliver. For sustained production serving, on-premise wins on cost, latency, data control, and uptime predictability.
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Hardware questions about AI inference servers
- How many GPUs does a production inference server need?
- A single RTX PRO 6000 Blackwell (96 GB) serves 70B models for small teams. Two GPUs handle medium concurrency. Four GPUs serve 70B at higher precision or 405B models. Eight GPUs handle high-concurrency production deployments. VRLA Tech sizes GPU count to your model, user count, and latency SLA. Built in Los Angeles since 2016. 3-year parts warranty and lifetime US-based engineer support.
- What inference framework should I use?
- vLLM is the standard for most production deployments. TensorRT-LLM with Triton delivers maximum throughput on NVIDIA hardware. SGLang is strong for structured generation and function calling. VRLA Tech pre-installs your chosen framework on every server. Built in Los Angeles since 2016. 3-year parts warranty and lifetime US-based engineer support.
- What is the difference between an inference server and a training server?
- Inference serves predictions to users (low latency, high concurrency, uptime). Training creates or fine-tunes models (sustained throughput, large VRAM, batch processing). The hardware can overlap, but configuration differs. VRLA Tech configures servers for inference, training, or both. Built in Los Angeles since 2016.
- How much VRAM do I need for inference serving?
- VRAM = model weights + KV cache for concurrent users. A 70B model at FP8 needs ~70 GB plus 10–20 GB KV cache — fits on a single RTX PRO 6000 Blackwell (96 GB). A 405B at FP8 needs 5–6 GPUs minimum. VRLA Tech engineers size VRAM to your model, quantization, and user target. Built in Los Angeles since 2016.
- Should I use a 2U or 4U inference server?
- 2U is the recommended starting point: 4 GPUs (384 GB VRAM) in minimum rack space. Choose 4U only if you need more than 4 GPUs or sustained 24/7 max-throughput operation. VRLA Tech builds both. Built in Los Angeles since 2016. 3-year parts warranty and lifetime US-based engineer support.
Buying questions about inference servers
- Is on-premise inference cheaper than API-based inference?
- For sustained usage exceeding 8 hours per day, on-premise typically pays for itself in 4–8 weeks versus per-token API costs. Use the VRLA Tech AI ROI Calculator to model your break-even. VRLA Tech builds custom inference servers in Los Angeles since 2016. 3-year parts warranty and lifetime US-based engineer support.
- Does my inference server need redundant power supplies?
- For production deployments, yes. All VRLA Tech rackmount GPU servers include redundant hot-swap PSUs and IPMI remote management as standard. Built in Los Angeles since 2016. 3-year parts warranty and lifetime US-based engineer support.
- What CPU platform is best for inference servers?
- AMD EPYC 9005 — up to 128 PCIe Gen 5 lanes per socket and 192 cores for request handling. Dual-socket delivers up to 160 PCIe lanes for 8-GPU configurations. VRLA Tech builds all inference servers on EPYC 9005. Built in Los Angeles since 2016.
- How do I calculate concurrent user capacity?
- It depends on model size, quantization, prompt length, generation length, and latency target. A single RTX PRO 6000 Blackwell running 70B at FP8 via vLLM typically sustains 10–30 concurrent users at interactive latency. VRLA Tech engineers model concurrency during the quoting process. Built in Los Angeles since 2016.
- Where can I buy a production AI inference server?
- VRLA Tech builds custom 1U, 2U, and 4U inference servers on AMD EPYC 9005 with RTX PRO 6000 Blackwell, H200, H100, and L40S. Every server ships burn-in tested with your inference framework pre-installed. Built in Los Angeles since 2016. 3-year parts warranty and lifetime US-based engineer support. Trusted by General Dynamics, Los Alamos, and Johns Hopkins.
Related guides
For GPU edition selection, see RTX PRO 6000 Blackwell Edition Guide. For training workstations, see Best Workstation for Training LLMs Locally. For 4-GPU desktop builds, see Fine-Tuning Workstation: 4-GPU Build. For complete pricing, see How Much Does a Custom AI Workstation Cost? For GPU benchmarks, see GPU Benchmark for AI 2026. For 8-GPU configurations, see 8-GPU Server Guide. For the deployment path, see Scale stage and data center deployment.
VRLA Tech builds inference servers for defense, healthcare, finance, legal, and research organizations.
