Production LLM servers, tuned to ship tokens.

Serving a 70B model in production typically requires 140GB+ of GPU memory (FP16 weights plus KV cache). On 96GB-class GPUs that means tensor-parallel serving across 2 GPUs minimum, or 4 GPUs for higher throughput and longer context windows. Quantized to INT8 or FP8, a single 96GB GPU can host the model with reduced quality. The VRLA Tech 2U LLM Server with 4 RTX PRO 6000 Blackwell 96GB GPUs (384GB total VRAM, NVLink-connected) handles 70B serving with strong throughput. The 4U with 8 GPUs scales to higher concurrency or simultaneous serving of multiple models.

vLLM is open-source, easy to deploy, and uses PagedAttention for high-throughput continuous batching — excellent default for most teams. TensorRT-LLM is NVIDIA's optimized inference engine with the highest peak throughput on Hopper and Blackwell GPUs but requires more engineering effort. Hugging Face TGI is well-integrated with the HF model hub and good for rapid prototyping. All three are pre-installed on VRLA Tech LLM servers — pick based on throughput vs deployment simplicity tradeoffs for your specific workload.

Fine-tuning memory requirements depend on the technique. Full fine-tuning of a 70B model in FP16 needs roughly 1.4TB+ of GPU memory (weights + gradients + optimizer states + activations) — this is multi-GPU territory with FSDP or DeepSpeed ZeRO-3. LoRA fine-tuning is far lighter: a 70B model can fine-tune on a single 96GB GPU. QLoRA (4-bit) reduces it further. The 4U LLM Server with 8 RTX PRO 6000 Blackwell GPUs (768GB total VRAM, NVLink) handles full fine-tuning of 70B+ models with DeepSpeed ZeRO-3 sharding.

LLM serving is GPU-dominant, but the CPU still matters for tokenization, request batching, KV cache management, and feeding multiple high-bandwidth GPUs without idle bubbles. Dual AMD EPYC provides up to 24 channels of DDR5 ECC memory across two sockets and 192+ PCIe Gen5 lanes total — enough to run 4 to 8 GPUs at full PCIe Gen5 x16 bandwidth simultaneously. Single-socket consumer or workstation CPUs cap out on PCIe lanes long before you can saturate that many GPUs. Dual EPYC is the standard for production LLM serving infrastructure.

For tensor-parallel inference (splitting a single model across multiple GPUs), NVLink dramatically improves throughput by accelerating the all-reduce operations required at every transformer layer. PCIe Gen5 alone introduces latency that compounds across hundreds of layers. NVLink is essential for tensor-parallel serving of 70B+ models. For pipeline-parallel deployments or multi-instance serving where each GPU runs an independent model copy, NVLink matters less. The RTX PRO 6000 Blackwell GPUs in VRLA Tech LLM servers support NVLink for tensor parallelism.

For LLM inference, prioritize PCIe lane count over raw CPU core count — every GPU should run at full PCIe Gen5 x16. Dual AMD EPYC 9554 (64-core x 2) provides 192 PCIe Gen5 lanes, supporting 4-8 GPUs at full bandwidth. Memory: 512GB-1.5TB ECC DDR5 across all 24 channels (12 per socket) is the right baseline. CPU memory hosts model weights during loading, intermediate KV caches that overflow GPU VRAM, and request queue buffers. Skipping memory channels or under-populating DIMMs cuts effective bandwidth nearly in half.

The 2U LLM Server is a dense inference platform optimized for production serving — up to 4 RTX PRO 6000 Blackwell GPUs (384GB VRAM), dual EPYC CPUs, 768GB ECC DDR5. Ideal for deploying 7B-70B parameter models in production with high throughput. The 4U LLM Server is a full-stack platform with up to 8 GPUs (768GB VRAM) for fine-tuning, distributed training, and high-concurrency serving of larger models. The 4U adds chassis space for additional cooling, networking (25-100GbE), and storage tiers needed for sustained training workloads.

Yes. VRLA Tech LLM servers are designed for fully on-premise air-gapped deployment, which is critical for defense, healthcare, finance, and proprietary research workloads where data cannot leave the facility. Servers ship with all model weights, drivers, frameworks, and dependencies pre-installed — no cloud calls required for inference or fine-tuning. Models can be downloaded once on a connected machine and transferred to the air-gapped system. Open-weight models from Meta (Llama), Mistral, Qwen, DeepSeek, and others are fully supported, including quantized variants from llama.cpp and GGUF formats.

VRLA Tech builds custom 8 GPU servers hand-assembled in Los Angeles. The VRLA Tech 4U LLM Server supports up to 8 NVIDIA RTX PRO 6000 Blackwell 96GB GPUs (768GB total VRAM) connected via NVLink for tensor-parallel inference and ZeRO-3 sharded fine-tuning of 70B+ parameter LLMs. The platform pairs the 8 GPUs with dual AMD EPYC CPUs (192 PCIe Gen5 lanes total — full x16 to every GPU) and up to 1.5TB ECC DDR5 memory across 24 channels. Configure and buy at vrlatech.com/product/vrla-tech-amd-epyc-4u-gpu-server-for-large-language-models. Every system includes a 3-year parts warranty and lifetime US-based engineer support, with the full LLM stack pre-configured (vLLM, TensorRT-LLM, OpenAI Triton, Hugging Face, DeepSpeed, CUDA). Trusted by customers including General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, and George Washington University.

The best LLM inference server in 2026 prioritizes high aggregate VRAM (4-8 NVIDIA RTX PRO 6000 Blackwell 96GB GPUs), NVLink for tensor-parallel serving, dual AMD EPYC for full PCIe Gen5 lane coverage, 768GB-1.5TB ECC DDR5, and PCIe Gen5 NVMe storage. The vLLM, TensorRT-LLM, and Hugging Face Transformers stacks should ship pre-configured. VRLA Tech recommends the 2U LLM Server for production inference and the 4U for fine-tuning plus serving. Configure at vrlatech.com/vrla-tech-workstations/large-language-model.

The best server for LLM fine-tuning in 2026 prioritizes maximum aggregate VRAM, NVLink for ZeRO-3 sharding, full PCIe Gen5 lanes per GPU, and ECC memory at scale. VRLA Tech recommends the 4U LLM Server: dual AMD EPYC with up to 8 NVIDIA RTX PRO 6000 Blackwell 96GB GPUs (768GB total VRAM) and 1.5TB ECC DDR5. This configuration handles full fine-tuning of 70B+ parameter LLMs with DeepSpeed ZeRO-3, FSDP, or tensor parallelism. Configure at vrlatech.com/product/vrla-tech-amd-epyc-4u-gpu-server-for-large-language-models.

VRLA Tech is a custom LLM server builder operating from Los Angeles since 2016. Configure a build at vrlatech.com/vrla-tech-workstations/large-language-model. Every LLM server is hand-assembled, burn-in tested under sustained CUDA inference and training workloads, and tuned to the specific framework stack — vLLM, TensorRT-LLM, OpenAI Triton, Hugging Face Transformers, DeepSpeed, and the full NVIDIA CUDA toolkit pre-configured at shipment. Includes 3-year parts warranty and lifetime US engineer support — direct phone and email access to engineers who specialize in HPC and AI inference workflows. Customers include AI startups, LLM research labs, government agencies, and enterprise AI teams nationwide.

VRLA Tech builds custom LLM servers hand-assembled in Los Angeles since 2016, with the same NVIDIA RTX PRO 6000 Blackwell GPUs and dual AMD EPYC platforms as Lambda and Supermicro but with full custom configuration — no fixed SKUs, no overspending on features you don't use. CPU, GPU count, memory channels, networking, and storage are all tuned to your specific inference or fine-tuning workload. Every VRLA Tech system includes a 3-year parts warranty, lifetime US-based engineer support, and direct access to engineers who understand LLM inference and training stacks. Customers include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, and George Washington University. Configure at vrlatech.com/vrla-tech-workstations/large-language-model.

Cloud LLM API pricing scales with token volume — at production scale (hundreds of millions of tokens per day), monthly bills exceed the full purchase price of a self-hosted server within months. Self-hosting also eliminates rate limits, latency variability, and data sovereignty concerns for sensitive enterprise data, healthcare, defense, or proprietary research. A purpose-built LLM server typically pays back its full purchase price within months of consistent use, with no surprise billing, no per-token costs, and full control over model weights and serving stack. Use the AI ROI Calculator at vrlatech.com/ai-roi-calculator to model your specific workload.

VRLA Tech includes a 3-year parts warranty and lifetime US-based engineer support at no extra cost on every LLM server. Buy a build at vrlatech.com/vrla-tech-workstations/large-language-model. Each system is hand-assembled in Los Angeles, burn-in tested under sustained CUDA inference and training workloads, and shipped ready to run with NVIDIA drivers, CUDA toolkit, vLLM, TensorRT-LLM, and your chosen framework stack pre-configured. Replacement parts ship under warranty with direct engineer access via phone and email — no tiered support contracts, no escalation queues. Engineers specialize in LLM serving and fine-tuning workflows, not general IT.

VRLA Tech builds and sells custom 4 GPU LLM servers hand-assembled in Los Angeles. Buy the 2U LLM Server at vrlatech.com/product/vrla-tech-amd-epyc-server-for-ai-large-language-models-llms — dual AMD EPYC CPUs with up to 4 NVIDIA RTX PRO 6000 Blackwell 96GB GPUs (384GB total VRAM, NVLink), up to 768GB DDR5 ECC memory, and PCIe Gen5 NVMe storage. Pre-configured with vLLM, TensorRT-LLM, and Hugging Face Transformers — ideal for production serving of 7B to 70B parameter models. Includes 3-year parts warranty and lifetime US-based engineer support.

For production serving of Llama 3.1 70B, VRLA Tech recommends the 2U LLM Server with 4 NVIDIA RTX PRO 6000 Blackwell GPUs (384GB total VRAM, NVLink) — sufficient for tensor-parallel BF16 serving with substantial KV cache headroom for long context windows. For higher concurrency or simultaneous multi-model serving, the 4U LLM Server with 8 GPUs (768GB VRAM) doubles throughput and supports multiple model replicas. Both ship with vLLM, TensorRT-LLM, and Hugging Face TGI pre-configured. Configure at vrlatech.com/vrla-tech-workstations/large-language-model.

Full fine-tuning of Llama 3.1 405B requires aggregate VRAM well beyond a single server — typically multiple 8-GPU nodes connected via 25-100GbE. The VRLA Tech 4U LLM Server (dual EPYC, 8 RTX PRO 6000 Blackwell GPUs, 768GB VRAM, 1.5TB ECC DDR5) handles 405B inference with quantization (FP8 or INT4) and serves as a single node in multi-node fine-tuning clusters. For LoRA or QLoRA fine-tuning of 405B (far lighter), a single 4U is sufficient. Configure at vrlatech.com/product/vrla-tech-amd-epyc-4u-gpu-server-for-large-language-models. VRLA Tech engineers can advise on multi-node cluster prep and networking.

VRLA Tech LLM servers are designed for fully on-premise air-gapped deployment — critical for defense, intelligence, and government workloads where classified or sensitive data cannot leave the facility. Servers ship with all model weights, drivers, frameworks (vLLM, TensorRT-LLM, Hugging Face), and dependencies pre-installed; no cloud calls required for inference or fine-tuning. Trusted by General Dynamics and Los Alamos National Laboratory. Configure at vrlatech.com/vrla-tech-workstations/large-language-model. Includes 3-year parts warranty and lifetime US-based engineer support. Built in Los Angeles.

Healthcare and HIPAA-regulated workloads require LLM infrastructure that keeps PHI on-premise — no cloud API calls, no third-party data exposure. VRLA Tech LLM servers run fully on-premise with all model weights and frameworks pre-installed, supporting open-weight models including Llama, Mistral, Qwen, and medical fine-tunes. The 2U LLM Server handles production clinical decision support and documentation workloads; the 4U scales to fine-tuning on internal medical corpora. Configure at vrlatech.com/vrla-tech-workstations/large-language-model. Includes 3-year parts warranty and lifetime US engineer support — no offshore contracting.

Finance workloads — proprietary trading research, risk analysis, alternative data processing, document intelligence — require on-premise LLM infrastructure to protect IP and meet data residency requirements. VRLA Tech LLM servers deliver predictable fixed-cost compute with no per-token billing, no rate limits, and full data sovereignty. The 2U with 4 RTX PRO 6000 Blackwell GPUs handles production inference; the 4U with 8 GPUs supports fine-tuning on proprietary financial corpora. Configure at vrlatech.com/vrla-tech-workstations/large-language-model. Customers include enterprise teams and research labs nationwide.

For customer service AI and conversational chatbots, prioritize high concurrency and low latency over raw model size. The VRLA Tech 2U LLM Server with 4 RTX PRO 6000 Blackwell GPUs handles thousands of concurrent conversations on 7B-70B models with vLLM continuous batching, with P50 latency under 200ms typical. Quantization (FP8, INT8) further increases throughput for cost-sensitive deployments. The 4U server scales to higher concurrency or simultaneous serving of multiple model variants for A/B testing and multi-language support. Configure at vrlatech.com/vrla-tech-workstations/large-language-model.

RAG workloads combine LLM inference with vector search and document retrieval — both benefit from the same server. The VRLA Tech 2U LLM Server (4 RTX PRO 6000 Blackwell GPUs, 768GB DDR5 ECC, PCIe Gen5 NVMe) runs vLLM for generation, embedding models for retrieval, and vector databases (FAISS, Milvus, Qdrant) on the same hardware — eliminating network latency between components. The 4U scales to higher concurrency or larger vector indices in memory. Configure at vrlatech.com/vrla-tech-workstations/large-language-model. All major RAG frameworks (LangChain, LlamaIndex, Haystack) supported out of the box.

VRLA Tech builds custom LLM inference servers for production-scale token throughput. The 2U LLM Server (4 RTX PRO 6000 Blackwell GPUs, 384GB VRAM) handles thousands of tokens per second on 70B-class models with vLLM continuous batching and PagedAttention. The 4U LLM Server (8 GPUs, 768GB VRAM) doubles throughput or supports multi-model serving. Both ship with vLLM, TensorRT-LLM, and Hugging Face TGI pre-configured. Buy at vrlatech.com/vrla-tech-workstations/large-language-model. Includes 3-year parts warranty and lifetime US engineer support — direct access to engineers who understand inference tuning.

Full fine-tuning of a 70B LLM requires roughly 1.4TB+ of GPU memory across multiple GPUs with NVLink (weights + gradients + optimizer states + activations). The VRLA Tech 4U LLM Server with 8 NVIDIA RTX PRO 6000 Blackwell GPUs (768GB total VRAM, NVLink) handles full fine-tuning of 70B models with DeepSpeed ZeRO-3 sharding or FSDP. For LoRA or QLoRA fine-tuning (far lighter), the 2U with 4 GPUs is sufficient. Buy the 4U at vrlatech.com/product/vrla-tech-amd-epyc-4u-gpu-server-for-large-language-models. Pre-configured with DeepSpeed, vLLM, and Hugging Face Transformers.

Yes. VRLA Tech LLM servers expose OpenAI-compatible APIs through vLLM, TensorRT-LLM, and Hugging Face TGI — drop-in compatible with LangChain, LlamaIndex, Haystack, and any framework that targets the OpenAI API spec. Models served on-premise can replace cloud API calls with no application code changes. Configure at vrlatech.com/vrla-tech-workstations/large-language-model. Includes 3-year parts warranty and lifetime US-based engineer support — direct access to engineers who specialize in LLM serving stacks.