Choose the Right
AI Hardware
for Your Enterprise
A structured buyer's guide comparing Cisco and NVIDIA AI infrastructure across training, inference, and blended workloads. From departmental pilots to enterprise AI factories.
Platform Overview
Two parallel recommendation frameworks — NVIDIA for direct GPU infrastructure, Cisco for validated enterprise designs with integrated networking, storage, and operations.
From DGX Spark prototyping to DGX B200 AI factory-class platforms. GPU-first architecture with maximum compute density.
DGX Spark
Development, prototyping, local private AI, model evaluation
RTX PRO 6000 Blackwell SE
Default enterprise on-prem AI — inference, RAG, fine-tuning
DGX Station
Premium single-box for heavier local training and inference
DGX B200
AI platform / AI factory — serious training and high-throughput inference
From UCS RTX PRO servers to Secure AI Factory with C885A M8. Enterprise-validated designs with Intersight operations.
RTX PRO Server (UCS C240 M8)
Entry point for private AI — right-sized for departmental use
AI RAG Augmented Inference Pod
Mid-market step-up for private inference plus retrieval
AI Scale Up Inference Pod
Enterprise-grade production inference tier
AI POD with C885A M8
Full-lifecycle training, fine-tuning, and inference at scale
Design Principles
Memory Bandwidth First
Favor high GPU memory bandwidth when inference throughput matters most.
96 GB+ Per GPU
Larger per-GPU memory for bigger models, longer contexts, and multiple endpoints.
Split Workloads Early
Separate training from production inference once deployments become material.
Full-Stack Thinking
Networking, storage, orchestration, observability, and security are core — not afterthoughts.
Recommendation Matrix
Tiered architectures for every scale — from departmental pilots to enterprise AI factories. Select a use case and vendor to explore the right tier for your needs.
Model adaptation, RAG pipelines, fine-tuning, and post-training workflows organized by workload size.
NVIDIA
5 tiersCisco
4 tiers
Find Your Solution
Answer four questions and we'll recommend the right AI infrastructure tier for your enterprise — with growth paths built in.
What is your primary AI workload?
Full-Stack Solution Design
A client-ready AI solution is not just a GPU server. Every layer of the stack must be designed intentionally — from compute to security.
Compute / GPU
Layer 1 of 5The foundation of your AI infrastructure — GPU count, memory, bandwidth, and interconnect.
Number of GPUs and GPU memory per device
GPU memory bandwidth for inference throughput
GPU-to-GPU interconnect for training scale
Workload partitioning and isolation capabilities
Sales Guidance
Recommended positioning for each vendor — from default starting recommendations to strategic platform options. Start with the workload, not the box.
NVIDIA
Recommended sales positioning
4x RTX PRO 6000 Blackwell Server Edition
Default starting recommendation for clients that want one strong private AI appliance with room to grow
DGX Spark
Development, prototyping, local private AI, model evaluation, light inference, small RAG
DGX Station
Premium single-box deskside option for heavier local training + inference
DGX B200 / AI Factory
Strategic scale for large training and high-throughput inference
Cisco
Recommended sales positioning
Cisco RTX PRO Server on UCS C845A M8
Best starting point for many private AI deployments; right-sized and easier to sell through Cisco-led channels
Cisco AI RAG Augmented Inference Pod
Strong mid-market step-up for private inference plus retrieval
Cisco AI Scale Up Inference Pod
Enterprise-grade production inference tier
Cisco AI POD with C885A M8
Full-lifecycle training, fine-tuning, and inference for AI factory-style deployments
Client Conversation Framework
Recommended narrative for enterprise buyers
Start with the workload, not the box
Understand what the client needs to accomplish before recommending hardware.
Identify the primary driver
Training / fine-tuning / RAG, inference, or blended training + inference.
Size the architecture
Model ambition, expected concurrency, data gravity / RAG demands, and operational maturity.
Build in room to grow
Extra GPU headroom, shared storage, 100GbE+ networking, and a clean expansion path.
Important Framing
These recommendations are design targets, not hard performance guarantees. Actual capacity depends on model family and size, quantization level, context length and KV cache usage, batch size and target latency, token generation rate goals, interactive vs. batch traffic mix, RAG retrieval overhead, and whether training and production inference share the same hardware.