Local AI computing
Three common ways to run AI workloads: local unified-memory systems, cloud GPU instances, and dedicated NVIDIA RTX workstations. Each has different strengths and ownership trade-offs.
Strix Halo vs. cloud GPU vs. RTX workstation
| Dimension | Strix Halo local | Cloud GPU | RTX workstation |
|---|---|---|---|
| Memory model | 128 GB unified memory | Split vRAM / system RAM | Discrete VRAM + system RAM |
| Data privacy | Data stays on your hardware | Sent to provider | Data stays on your hardware |
| Latency | Local, predictable | Network dependent | Local, predictable |
| Operating cost | One-time hardware cost | Per-hour or subscription | One-time hardware cost |
| CUDA / TensorRT | Not available | Available on NVIDIA instances | Available |
| Setup effort | Moderate; ROCm / iGPU stack | Low; provider-managed | Moderate; CUDA stack |
| Typical fit | Private LLM, RAG, agents, homelab | Burst training, large models | Fast inference, CUDA workflows |
Where local unified memory wins
Privacy-by-default workflows
Documents, prompts, and model weights stay on hardware you control. Suitable for sensitive RAG, legal, medical, or proprietary research use cases.
Predictable long-term cost
No per-token or per-hour cloud fees. The cost model is hardware plus electricity, which can be favorable for sustained daily usage.
Large single-model memory
128 GB of unified memory lets you load a single 70B-class quantized model with room for context and KV cache, without model parallelism.
Offline and low-latency access
Local inference does not depend on internet latency or provider availability. Useful for always-on agents and homelab servers.
Where other options are stronger
- • Maximum CUDA image-generation throughput — RTX 4090 / 5090 systems are typically faster for Flux / SDXL.
- • Large-scale model training — cloud clusters or multi-GPU RTX / datacenter systems are more suitable.
- • Zero setup — managed cloud APIs require the least configuration.
- • Lowest upfront cost — hourly cloud usage avoids hardware purchase.