What is NVLink?
NVLink is NVIDIA's high-speed interconnect enabling GPU-to-GPU communication at up to 900GB/s for multi-GPU training. NVLink bandwidth is critical for distributed training performance.
This AI hardware and semiconductor term is currently being developed. Detailed content covering technical specifications, performance characteristics, use cases, and purchasing considerations will be added soon. For immediate guidance on AI infrastructure strategy, contact Pertama Partners for advisory services.
NVLink interconnect performance determines whether multi-GPU training investments deliver expected computational scaling or waste capacity on inter-GPU communication overhead. Organizations building training infrastructure with 4+ GPUs should prioritize NVLink-enabled configurations since communication bottlenecks degrade scaling efficiency by 30-50% on PCIe-only systems. The technology choice impacts total cost of ownership over 3-5 year hardware cycles since NVLink-optimized training completes faster, reducing cloud rental or facility operational costs proportionally. Southeast Asian data centers offering NVLink-equipped GPU clusters attract premium AI training customers willing to pay 20-30% higher rental rates for guaranteed scaling performance.
- GPU-to-GPU direct interconnect.
- 900GB/s per GPU (H100 NVLink4).
- Much faster than PCIe for GPU communication.
- Essential for multi-GPU training.
- NVSwitch enables all-to-all connectivity.
- NVIDIA proprietary (vs open standards).
- Fourth-generation NVLink delivers 900GB/s bidirectional bandwidth per GPU, enabling multi-GPU systems to operate as unified memory pools for large model training workloads.
- NVLink Switch systems connecting up to 256 GPUs within single nodes create massive shared memory architectures eliminating distributed training communication bottlenecks.
- Cost premium of $5,000-10,000 per GPU for NVLink-enabled configurations must be justified through workload analysis confirming multi-GPU communication is actual performance bottleneck.
- NVLink topology selection between bridge and switch configurations depends on GPU count requirements with bridges suitable for 2-8 GPU systems and switches for larger clusters.
- Vendor lock-in to NVIDIA ecosystem becomes permanent when NVLink infrastructure investments preclude migration to AMD alternatives using different interconnect architectures.
- Fourth-generation NVLink delivers 900GB/s bidirectional bandwidth per GPU, enabling multi-GPU systems to operate as unified memory pools for large model training workloads.
- NVLink Switch systems connecting up to 256 GPUs within single nodes create massive shared memory architectures eliminating distributed training communication bottlenecks.
- Cost premium of $5,000-10,000 per GPU for NVLink-enabled configurations must be justified through workload analysis confirming multi-GPU communication is actual performance bottleneck.
- NVLink topology selection between bridge and switch configurations depends on GPU count requirements with bridges suitable for 2-8 GPU systems and switches for larger clusters.
- Vendor lock-in to NVIDIA ecosystem becomes permanent when NVLink infrastructure investments preclude migration to AMD alternatives using different interconnect architectures.
Common Questions
Which GPU should we choose for AI workloads?
NVIDIA dominates AI with H100/A100 for training and A10G/L4 for inference. AMD MI300 and Google TPU offer alternatives. Choose based on workload (training vs inference), budget, and ecosystem compatibility.
What's the difference between training and inference hardware?
Training needs high compute density and memory bandwidth (H100, A100), while inference prioritizes latency and cost-efficiency (L4, A10G, TPU). Many organizations use different hardware for each workload.
More Questions
H100 GPUs cost $25K-40K each, typically deployed in 8-GPU nodes ($200K-320K). Cloud rental is $2-4/hour per GPU. Inference hardware is cheaper ($5K-15K) but you need more units for serving.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
Chiplet Architecture combines multiple smaller dies into single package improving yields and enabling mix-and-match of technologies. Chiplets enable cost-effective scaling of AI accelerators.
HBM provides extreme memory bandwidth through 3D stacking and wide interfaces, essential for AI accelerators to feed compute units. HBM bandwidth determines large model training and inference performance.
InfiniBand provides low-latency high-bandwidth networking for AI clusters enabling efficient distributed training across hundreds of GPUs. InfiniBand is standard for large-scale AI training infrastructure.
AI Supercomputers combine thousands of GPUs with high-speed networking for training frontier models, representing peak AI infrastructure. Supercomputers enable capabilities beyond commodity cloud infrastructure.
AI Data Centers provide specialized infrastructure for AI workloads with high-density compute, cooling, and power delivery. Purpose-built AI data centers address unique requirements of GPU clusters.
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