By VRLA Tech  ·  AI Infrastructure  ·  April 2026

When organizations move from cloud GPU rental to on-premise LLM infrastructure, the most common configuration question is straightforward: do I need a 4-GPU server or an 8-GPU server? The answer depends on three things — the models you are running, the concurrent user load you are serving, and whether you are training, fine-tuning, or running inference. This guide cuts through the confusion and gives you a direct answer for the most common LLM workloads in 2026.

The foundation: VRAM is the primary constraint

Before comparing 4-GPU and 8-GPU configurations, you need to understand why GPU count matters for LLM workloads. The primary constraint is VRAM — the memory on the GPU that holds model weights, activations, and the KV cache during inference.

Large language models are large. LLaMA 3 70B at full FP16 precision requires approximately 140GB of VRAM just to hold the model weights. Add KV cache for concurrent requests and you need 160–200GB or more for production serving. No single GPU in 2026 — including the NVIDIA RTX PRO 6000 Blackwell with 96GB VRAM — has enough VRAM for a 70B model alone. You need multiple GPUs working together with tensor parallelism to distribute the model across their combined VRAM.

This is the core reason GPU count matters for LLM servers: more GPUs means more combined VRAM, which means larger models, higher precision, more concurrent requests, and more headroom for KV cache growth under load.

VRAM requirements for common LLM workloads in 2026

The VRLA Tech 4-GPU EPYC LLM server

The VRLA Tech 4-GPU EPYC LLM Server runs AMD EPYC 9375F with four NVIDIA RTX PRO 6000 Blackwell GPUs. Combined VRAM: 384GB. Combined memory bandwidth: exceptional, with GDDR7 across all four GPUs connected via PCIe 5.0 to the EPYC platform.

What the 4-GPU server handles

• Full FP16 inference on 70B models: 384GB of combined VRAM comfortably holds a 70B model at full FP16 precision with substantial KV cache headroom for concurrent requests. LLaMA 3 70B, Qwen 2.5 72B, and similar-sized models run at full precision without quantization compromise.
• Multi-user concurrent serving: With vLLM’s paged attention and continuous batching, the 4-GPU server handles dozens of concurrent users on a 70B model with good throughput. The exact concurrent user ceiling depends on context window length and generation length.
• LoRA and QLoRA fine-tuning up to 70B: Fine-tuning a 70B model with QLoRA (4-bit quantization of the base model) requires 48–96GB of VRAM depending on batch size and sequence length. The 4-GPU configuration provides comfortable headroom for 70B QLoRA fine-tuning jobs.
• Multi-model serving for smaller models: Running multiple 7B or 13B models simultaneously — for A/B testing, specialized model routing, or multi-tenant deployments — fits comfortably within 384GB combined VRAM.
• Embedding generation at scale: Generating embeddings for large document corpora with models like BGE, E5, or custom embedding models is a high-throughput GPU workload that the 4-GPU configuration handles efficiently.

What the 4-GPU server does not handle

• Full FP16 inference on models larger than approximately 180B parameters without quantization
• Full parameter fine-tuning (non-LoRA) of 70B models, which requires significantly more VRAM than inference
• Extremely high concurrent user loads on 70B models where per-request KV cache consumption exhausts available VRAM
• Running two simultaneous 70B models for multi-tenant deployments with model isolation

The VRLA Tech 8-GPU EPYC LLM server

The VRLA Tech 4U 8-GPU EPYC Server runs dual AMD EPYC 9375F processors with up to eight NVIDIA RTX PRO 6000 Blackwell GPUs. Combined VRAM: 768GB. Combined memory bandwidth: approximately double the 4-GPU configuration.

What the 8-GPU server handles

• Full FP16 inference on models up to 150B+ parameters: 768GB of combined VRAM handles foundation models significantly larger than 70B at full precision. LLaMA 3 405B runs at FP8 precision within this configuration with excellent throughput.
• Enterprise-scale multi-tenant inference: Serving hundreds of concurrent users on 70B models requires the KV cache capacity that 768GB of VRAM provides. Enterprise deployments with SLA requirements for response latency at high concurrency need the headroom the 8-GPU configuration provides.
• Multiple simultaneous 70B model deployments: Running two isolated 70B models simultaneously — for multi-tenant deployments where different customers or applications require different models — fits within 768GB of combined VRAM with room for KV cache on each.
• Full parameter fine-tuning of 13B–30B models: Full parameter fine-tuning requires significantly more VRAM than LoRA-based approaches. The 8-GPU configuration provides the VRAM capacity for full parameter training on mid-sized models.
• Pre-training small models from scratch: Teams building custom foundation models from scratch on proprietary datasets can use the 8-GPU server for pre-training runs on models up to approximately 7B–13B parameters within a practical training time window.
• Research-scale experimentation: Research teams running large-scale experiments comparing multiple model configurations, fine-tuning approaches, or inference optimization strategies benefit from the maximum VRAM and compute capacity the 8-GPU configuration provides.

Side by side comparison

Decision framework: which server is right for you

The right configuration depends on three questions. Work through them in order.

Question 1: What is the largest model you need to run at full precision?

If your largest model is 70B parameters or smaller at FP16, the 4-GPU server handles it. If you need to run models larger than approximately 180B at FP16, or 350B at FP8, you need the 8-GPU server. If you are not sure what models you will need in 12 months, the 8-GPU server provides more headroom for model scale increases without a hardware upgrade.

Question 2: What is your concurrent user load?

For internal tools, research teams, and small to mid-size production applications serving tens of concurrent users on 70B models, the 4-GPU server provides sufficient throughput. For enterprise applications serving hundreds of concurrent users, multi-tenant platforms with SLA requirements, or public-facing AI applications with variable peak loads, the 8-GPU configuration provides the throughput headroom you need.

Question 3: Are you fine-tuning or only running inference?

If you are running inference only, the 4-GPU server handles 70B models well. If you are fine-tuning 70B models with QLoRA, the 4-GPU server has enough VRAM. If you need full parameter fine-tuning of 70B models, or fine-tuning of larger models, the 8-GPU server is required.

When to start with 4-GPU and upgrade to 8-GPU

Many teams start with a 4-GPU server and expand as their workload grows. This is a rational approach when your current production load fits within 4-GPU capacity but you anticipate growth. VRLA Tech engineers can help you plan an upgrade path from the 4-GPU to the 8-GPU configuration as your requirements evolve.

The considerations for staged deployment include rack space planning — adding an 8-GPU server alongside a 4-GPU server requires 4U of additional rack space — power capacity planning, and networking architecture for load balancing inference traffic across multiple servers.

Quantization: how it changes the VRAM calculation

Quantization reduces model weight precision — from FP16 (16-bit) to FP8 (8-bit) or INT4 (4-bit) — which proportionally reduces VRAM requirements. Understanding how quantization affects the 4-GPU vs 8-GPU decision is important because many production LLM deployments use quantized models to fit larger models into available VRAM or to increase throughput.

• FP8 quantization reduces VRAM by approximately 50% compared to FP16 with minimal quality loss for most use cases. LLaMA 3 70B at FP8 requires approximately 70GB of VRAM, fitting on a single RTX PRO 6000 with room for KV cache.
• INT4 / GPTQ / AWQ quantization reduces VRAM by approximately 75% compared to FP16. LLaMA 3 70B at INT4 requires approximately 35–40GB of VRAM. Quality loss is more significant than FP8 but acceptable for many applications.
• GGUF quantization for CPU inference (Q4, Q5, Q8 variants) allows LLM inference on CPU without GPU. Quality varies by quantization level. Suitable for low-throughput deployments where GPU cost is not justified.

If your use case tolerates FP8 quantization and your largest model is 70B, a single RTX PRO 6000 Blackwell may be sufficient for inference. If you need full FP16 precision for quality-sensitive production applications, the multi-GPU configurations described above are the path forward.

Pre-validated frameworks on VRLA Tech LLM servers

Both the 4-GPU and 8-GPU VRLA Tech LLM servers ship pre-validated for the three primary LLM serving frameworks in production use in 2026:

• vLLM: The most widely deployed open-source LLM inference framework. Paged attention, continuous batching, tensor parallelism across multiple GPUs, and support for all major open-weight models. VRLA Tech validates CUDA toolkit and vLLM version compatibility before shipping.
• TensorRT-LLM: NVIDIA’s high-performance LLM inference engine. Delivers maximum throughput for production deployments by compiling models into optimized TensorRT engines. Best-in-class performance for NVIDIA GPU deployments.
• Text Generation Inference (TGI): Hugging Face’s production LLM serving framework. Strong ecosystem integration with the Hugging Face model hub and straightforward deployment for teams already using Hugging Face tooling.

Pre-validation means the framework is installed, configured, and tested with a representative model on your specific hardware configuration before the system ships. You are not starting from scratch with CUDA installation and driver configuration when the server arrives.

VRLA Tech has been building custom AI workstations and GPU servers since 2016. All systems ship with a 3-year parts warranty and lifetime US-based engineer support.