Production LLM servers, tuned to ship tokens.

It depends on precision, context length, concurrency, and the serving engine. A 70B-class model at BF16 is roughly 140GB for weights alone before KV cache and framework overhead, so it generally requires model sharding across multiple high-memory GPUs. Quantization can reduce the memory requirement substantially. VRLA Tech sizes GPU count and memory around the actual model and throughput target rather than using a fixed 70B configuration.

GPU memory requirements are driven by model weights, numerical precision, KV cache, context length, batching, and concurrency. A smaller quantized model may fit comfortably on one 96GB GPU, while large BF16 models or high-concurrency serving may require several GPUs. Aggregate VRAM is useful only when the software explicitly shards the model or workload across those GPUs.

Fine-tuning requirements vary dramatically by method. LoRA and QLoRA can reduce memory needs enough for many workflows to run on one or a few high-memory GPUs. Full-parameter fine-tuning requires substantially more memory for model states, gradients, optimizer states, and activations, and large models may require sharding, CPU offload, or multiple nodes. VRLA Tech can size the system around the specific model, precision, sequence length, and fine-tuning method.

vLLM is a common choice for flexible high-throughput serving and OpenAI-compatible APIs. TensorRT-LLM is NVIDIA's optimized inference stack for supported GPUs and can be attractive when teams want to tune latency and throughput aggressively. Hugging Face TGI integrates closely with the Hugging Face ecosystem. The right choice depends on model support, deployment simplicity, latency, throughput, quantization, and your existing software environment.

Not every multi-GPU LLM workload requires NVLink. Tensor-parallel and some distributed-training workloads can benefit from faster GPU-to-GPU communication, while independent model replicas or pipeline-parallel designs may be less dependent on it. RTX PRO 6000 Blackwell Server Edition systems can support different interconnect topologies depending on the server design. VRLA Tech selects PCIe, PCIe-switch, NCCL, networking, and—where supported—NVLink topology around the actual communication pattern.

Dense GPU servers need high PCIe lane counts, memory bandwidth, ECC memory capacity, and enough CPU resources for tokenization, data loading, request handling, storage, and networking. AMD EPYC server platforms are well suited to these requirements. The exact GPU link width and routing still depend on the CPU generation, motherboard, PCIe switches, and chassis, so VRLA Tech validates the complete platform rather than relying on CPU specifications alone.

The right form factor depends on GPU count, GPU power, cooling, storage, networking, serviceability, and rack constraints—not only compute density. VRLA Tech's featured 2U and 4U platforms scale to 4 and 8 RTX PRO 6000 Blackwell Server Edition GPUs, while custom rackmount configurations can scale to 10 GPUs when the chassis and power design support it. We recommend the layout based on the workload and facility requirements.

Yes. LLM systems can be configured for fully local inference and fine-tuning with no dependency on a cloud API. For air-gapped environments, required model weights, drivers, containers, packages, and dependencies need to be staged and validated before deployment or transferred through the customer's approved process. This can be useful for proprietary, regulated, research, government, or other sensitive workloads.

Yes. A properly sized server can host the generation model alongside embedding or reranking models and components such as FAISS, Milvus, Qdrant, or other vector-search services. Whether those services should share one machine or be separated depends on dataset size, latency requirements, availability goals, GPU utilization, and operational architecture.

Self-hosting is most attractive when workloads are sustained and predictable, data or model weights need to remain under your control, or dedicated capacity matters. Cloud APIs and rented GPUs can still be better for bursty, experimental, or rapidly changing workloads. The break-even point depends on utilization, model size, power, support, cloud/API pricing, and hardware cost. Use the VRLA Tech AI ROI Calculator to compare scenarios.

VRLA Tech LLM servers include a 3-year parts warranty and lifetime US-based engineer support. Systems are assembled and burn-in tested before shipment. When software installation is part of the order, VRLA Tech can also validate the requested driver, CUDA, framework, and container environment against the delivered hardware configuration.