Quick Verdict: Edge AI vs Centralized GPU
Edge AI runs models on local hardware (phones, embedded devices, edge servers) with zero network latency but severe compute constraints. Centralised GPU inference runs on powerful dedicated GPU servers with virtually unlimited model size but requires a network round trip. Edge handles 1-7B parameter models at 10-30 tokens per second. A centralised RTX 6000 Pro serves 70B models at 50+ tokens per second. Choose edge when network latency exceeds model compute time. Choose centralised for everything else.
Architecture Differences
Edge AI deploys quantised models onto client devices or local servers close to users. Models must fit within 4-16GB of device memory, limiting practical size to 7B parameters at 4-bit quantisation. Frameworks like Ollama and llama.cpp make edge deployment practical for small models.
Centralised GPU inference consolidates all compute in a data centre. Clients send requests over the network, a GPU server processes them, and results stream back. vLLM on a single RTX 6000 Pro serves dozens of concurrent users with models up to 40B at full precision. Multi-GPU clusters scale to hundreds of billions of parameters.
Performance Comparison
| Metric | Edge (Phone/Laptop) | Edge Server (RTX 5090) | Centralised (RTX 6000 Pro 96 GB) |
|---|---|---|---|
| Max Model Size | 3-7B (quantised) | 70B (4-bit) | 40B (FP16), 70B+ (quantised) |
| Throughput (7B Q4) | 10-30 tok/s | 80-120 tok/s | 150+ tok/s |
| Network Latency | 0ms | 1-5ms (LAN) | 20-100ms (WAN) |
| Concurrent Users | 1 | 5-15 | 30-100 |
| Privacy | Data stays on device | Data stays on-premise | Data traverses network |
| Power Consumption | 5-15W | 300-450W | 300-700W |
| Cost per Device | User’s existing hardware | 2,000-3,000 | Monthly server rental |
Latency Analysis
Edge wins on first-token latency when the network round trip exceeds GPU compute time. For a 3B model generating a response, edge devices produce the first token in 50-100ms. Centralised inference adds 20-100ms of network latency on top of GPU compute time. For applications like real-time voice assistants or offline-capable tools, this network penalty is unacceptable. Review our GPU benchmarks for detailed latency data.
For complex tasks requiring 70B+ models or RAG pipelines with vector search, edge devices simply cannot run the workload. Centralised GPU inference is the only viable option for large model deployment. The network latency is a small price for dramatically better model quality.
When to Choose Each
Edge AI: Offline-capable applications, extreme latency sensitivity under 50ms, data that cannot leave the device (healthcare, military), simple tasks served by small models, and mobile applications. See tutorials for edge deployment guides.
Centralised GPU: Models above 13B parameters, multi-model pipelines, high concurrency, production LLM serving, tasks requiring maximum model quality, and any workload where 50-150ms round-trip latency is acceptable.
Recommendation
Most production AI applications benefit from centralised GPU inference. The model quality gap between 7B edge models and 70B centralised models outweighs the latency advantage for most use cases. Use edge only when offline capability or sub-50ms latency is non-negotiable. Deploy centralised inference on GigaGPU dedicated servers with private AI hosting for data-sensitive workloads. Visit the infrastructure blog for deployment patterns.
