Running LLMs Locally on Consumer Hardware — Part 1: The Stack and First Benchmarks

This is the first in a series of build-log posts documenting a local LLM project, in which models are run on owned consumer hardware rather than through a cloud API. The present entry covers the hardware, the software stack, and the benchmarks by which a primary model was selected. The hardware Two machines are used, both consumer-grade. All benchmarks reported below were obtained on the primary desktop. Machine CPU RAM GPU Primary desktop Ryzen 5950X ~80 GB DDR4 AMD RX 6900XT (16 GB) Secondary box Ryzen 5600G 32 GB NVIDIA GTX 1060 (6 GB) The software stack Ollama serves as the model runner across two GPU vendors: ROCm 5.7 for the AMD card on the primary desktop, and CUDA for the NVIDIA card on the secondary box. The primary model is Gemma 4 26B, a mixture-of-experts model with roughly 3.8B active parameters, quantized to Q4_K_M and occupying approximately 18 GB on disk. On the RX 6900XT it is run with an automatic GPU/CPU layer split, as the Q4 weights together with the KV cache exceed the 16 GB of available VRAM. Several Ollama settings were enabled to recover headroom: flash attention, and an 8-bit ( q8_0 ) KV cache, the latter approximately halving the cache footprint. A free cloud tier is retained for occasional heavier tasks, though the objective is to run as much as possible locally. Selecting a model: benchmarks Before a primary model was chosen, the installed models were benchmarked. Two properties were of interest: throughput and output quality. Throughput was measured on the primary desktop with a 500-word essay prompt ( ollama run <model> --verbose ): Model Tokens/sec Duration Tokens out gemma4:26b 18.86 50.11s 945 gemma4-26b (64K ctx) 17.96 51.99s 934 mistral:7b-instruct 34.81 10.17s 354 llama3.2 57.11 3.99s 228 The smaller models are substantially faster; their token counts, however, are lower, and in practice their responses were correspondingly shallower. Quality was assessed with a five-task suite spanning logic, coding, summarization, creative writ