Specification Before Procurement
GPU hardware for AI inference represents a significant capital investment. Over-provisioning wastes budget on compute that sits idle. Under-provisioning creates bottlenecks that frustrate users and limit adoption. We profile your actual workload, model your concurrency requirements, and specify the exact hardware configuration that delivers your target performance at the lowest total cost of ownership.
Workload Profiling
We analyze your inference patterns: model sizes, token volumes, concurrency peaks, latency requirements, and growth projections. This data drives hardware decisions, not assumptions or vendor recommendations.
GPU Selection
Match GPU capabilities to workload requirements. NVIDIA H100 for maximum throughput, A100 for proven reliability, L40S for mixed inference/graphics, consumer GPUs for development and testing environments.
Memory Sizing
GPU VRAM determines the largest model you can serve. System RAM affects batch sizes and KV cache capacity. We calculate exact memory requirements for your model at your target quantization level and concurrency.
Vendor Recommendations
Specific SKUs from Dell, Supermicro, NVIDIA, HPE, and Lenovo with pricing estimates. We recommend based on your existing vendor relationships, support requirements, and deployment timeline.
Hardware Specification Process
Profile
Analyze workload requirements
Model
Calculate compute and memory needs
Specify
Select SKUs and configurations
Validate
Benchmark before full procurement
Profile
Analyze workload requirements
Model
Calculate compute and memory needs
Specify
Select SKUs and configurations
Validate
Benchmark before full procurement
Hardware Specification Tiers
GPU Selection Guide
The GPU market for AI inference includes options ranging from $2,000 consumer cards to $40,000 data center accelerators. The right choice depends on your model size, throughput requirements, reliability needs, and budget constraints.
NVIDIA H100 SXM (80 GB HBM3). The highest-throughput option for production inference. FP8 Transformer Engine, NVLink 4.0, and 3.35 TB/s memory bandwidth. Best for organizations running 70B+ models at high concurrency or requiring the absolute lowest latency per token. Price: $25,000-35,000 per GPU.
NVIDIA A100 (80 GB HBM2e). The proven enterprise standard. Excellent price-to-performance for models up to 70B at INT8. Widely available on the secondary market at significant discounts. NVLink 3.0 at 600 GB/s. Best for organizations prioritizing reliability and cost efficiency over maximum throughput. Price: $10,000-15,000 per GPU.
NVIDIA L40S (48 GB GDDR6). PCIe form factor fits standard servers without NVLink baseboard. Strong inference throughput at a lower price point. Best for organizations with standard server infrastructure that want to add AI capability without specialized GPU servers. Price: $7,000-10,000 per GPU.
Consumer GPUs (RTX 4090, 5090). 24 GB VRAM handles 13B models at FP16 or 70B at aggressive quantization. No ECC memory, no NVLink, limited vendor support. Appropriate for development, testing, and proof-of-concept environments. Not recommended for production serving. Price: $1,500-2,000 per GPU.
Beyond the GPU
The GPU gets the headlines, but CPU, memory, storage, and networking all affect inference performance and reliability.
CPU. Pre-processing, tokenization, and post-processing run on the CPU. AMD EPYC 9004 or Intel Sapphire Rapids with high core counts. For CPU-offload inference (GGUF models), clock speed matters more than core count.
System memory. Minimum 2x GPU VRAM for CPU-offload scenarios. 512 GB to 2 TB DDR5 for servers running multiple model instances with large KV caches. ECC memory is mandatory for production systems.
Storage. NVMe SSDs for model weight loading. A 405B model at FP16 occupies ~800 GB. Fast storage reduces cold-start time from minutes to seconds. RAID 1 or RAID 10 for reliability.
Who This Is For
Hardware specification consulting is for organizations planning their first GPU infrastructure purchase or expanding existingcapacity. We help you avoid the two most common mistakes: buying what a vendor recommends (over-provisioned) or buying what fits the budget (under-provisioned).
Contact us at ben@oakenai.tech