Open-source large language models promise data sovereignty and lower latency, but public API benchmarks rarely reflect local hardware setups in South America. Running inference on local GPUs in Santiago reveals stark differences between marketed token generation speeds and real-world throughput under memory bandwidth constraints.
Hardware Setup and Test Methodology
We evaluated open-weight models on dedicated workstation GPUs inside a local test environment. Instead of relying on synthetic batch queries, we tested single-stream latency using realistic developer prompts that simulate daily coding assistant workloads.
Memory Bottlenecks and Real Throughput
The bottleneck for localized inference rarely lies in raw compute power, but in memory bandwidth during high-context processing. Quantized eight-bit variants delivered acceptable generation rates, while unquantized floating-point weights stalled heavily when context windows expanded past four thousand tokens.
Practical Guidelines for Local Deployment
Developers building AI-assisted tools in Chile should prioritize four-bit and eight-bit quantization schemes to keep memory overhead manageable. Skipping expensive cloud API calls is entirely viable if your architecture accounts for context pruning and explicit cache reuse.