No products in the cart.
AI & HPC Workstations
Large-Language Model (LLM) Servers
High-throughput inference and fine-tuning platforms: EPYC CPUs, 4’8 professional GPUs, ECC DDR5, PCIe 5.0 NVMe, and data-center cooling profiles.
LLM Server Configurations
Two expertly engineered systems cover mid-range to dense multi-GPU deployments.
LLM 4-GPU Server
Balanced multi-tenant inference, large context windows, and fine-tuning. Ideal for consolidating applications.
● CPU: 1 x AMD EPYC 9375F
● GPU: 4 x NVIDIA RTX PRO Blackwell Max-Q 96GB
● Memory: 768GB DDR5-5600 REG ECC
● VRAM: Configurable up to 384 GB
● Options: Supports NVIDIA RTX PRO & L40S GPUs, customizable storage tiers, 25/100GbE networking
LLM 8-GPU Server
High-density server for maximum concurrency, 100B+ models, and rapid A/B iterations across multiple deployments.
● CPU: 2 x AMD EPYC 9354
● GPU: 8 x NVIDIA RTX PRO Blackwell Server 96GB
● Memory: 1.5 TB DDR5-5600 REG ECC
● VRAM: Configurable up to 1,128 GB
● Options: Customizable storage tiers, 100GbE/InfiniBand networking, enterprise software stacks
LLM Server Solutions: Purpose-Built Hardware for High-Throughput Inference and Fine-Tuning
Our Large Language Model (LLM) Servers are custom-engineered for maximum performance, featuring AMD EPYC CPUs, 4-8 professional GPUs, ECC DDR5 memory, PCIe 5.0 NVMe storage, and data-center cooling profiles. We provide the predictable latency and massive multi-user concurrency required for production inference and on-prem fine-tuning.
Key Takeaway: The Pillars of LLM Performance
Factor
Component
Impact on LLM Performance
Model Size & Concurrency
GPU VRAM Capacity
Limits the size of the model that can be hosted and the batch size for inference.
Data Throughput
Interconnect Bandwidth (PCIe/NVLink)
Determines how fast model weights and tensors can be moved between components.
Staging & Checkpoints
Storage Throughput (NVMe)
Defines the speed of model loading, fine-tuning checkpoints, and feature store access.
Validated LLM Inference & Serving Stacks
Framework
Optimization
Key Benefit
PagedAttention, Tensor Parallelism
High-throughput multi-GPU inference with optimal memory utilization.
Kernel-level optimizations, FP8/BF16 Paths
Maximizes tokens/sec and lowers latency.
Dynamic batching, multi-model serving
Enterprise-grade serving with repository management.
Speculative decoding, tensor parallelism
Optimized text-generation-inference for transformers.
Model compression, graph optimizations
Latency-optimized inference at enterprise scale.
CUDA & ROCm
Validated stacks for both NVIDIA and AMD GPUs.
Buyer Guidance: Frequently Asked Questions (FAQ)
Platform Matters: Server-Grade Hardware for LLM Scaling
For high-density LLM serving, the platform architecture not just the raw CPU speed is critical. Server-grade motherboards are essential because they provide abundant PCIe lanes, ECC memory reliability, high DDR5 capacity, and support for dense GPU topologies. We commonly recommend AMD EPYC platforms for their superior memory channels and PCIe lane count, ideal for dense GPU configurations.
When Do CPU Cores Actually Matter for LLMs?
While GPUs handle token generation, CPUs handle the data pipeline and orchestration: ingestion, preprocessing, tokenization, embedding creation, and API orchestration. Plan for at least one high-performance CPU core per GPU, with additional headroom for databases or co-located services.
GPU & Memory Guidelines for LLM Servers
Why Professional GPUs Are Required: Professional GPUs (like NVIDIA RTX PRO and L40S) are required because they provide higher VRAM capacities, rackmount cooling profiles, and 24/7 duty cycle reliability. Multiple GPUs scale throughput and enable hosting of trillion-parameter models. NVLink is beneficial for GPU-to-GPU communication, but modern inference stacks can also leverage PCIe 5.0 effectively.
Do more CPU cores make LLMs faster?
No, token generation is primarily GPU-bound. Beyond one CPU core per GPU, additional cores provide little benefit unless data pipelines or databases are also hosted on the CPU.
Which CPUs are best for LLM serving Intel Xeon or AMD EPYC?
Both Intel Xeon and AMD EPYC are excellent. We recommend AMD EPYC for its memory channels and PCIe lane count, ideal for 8-GPU configurations.
How much VRAM and system memory do I need for my LLM?
VRAM defines the maximum model size and concurrency. For 70B-class models, expect hundreds of GB of VRAM. System RAM should be ~2? total VRAM.
Is NVLink required for LLM hosting?
No, NVLink is beneficial but not required. Modern inference stacks can utilize PCIe 5.0 for parallelism effectively.
What storage layout works best for LLM servers?
Use a tiered layout: dedicated NVMe for OS, RAID10 NVMe array for active models, and larger SSD/NAS tiers for datasets and archives.
Can I scale my LLM beyond a single server?
Yes, we can design clusters with 100/200GbE or InfiniBand, shared repositories, and orchestration via Kubernetes or Slurm for horizontal scaling.
Architect Your Custom LLM Server
Tell our engineers your target models, maximum context windows, and concurrency goals. We will map the optimal specs for the best tokens/sec per dollar.
