Hardware Testing Reveals VRAM Tier Performance for Local AI

A developer working on “Lemonade,” a local desktop assistant, has released a series of unverified performance benchmarks detailing how various hardware configurations handle large language models (LLMs). The findings, shared via a Reddit post, aim to provide empirical data on video RAM (VRAM) requirements for local AI applications, challenging some common assumptions regarding hardware limitations.

VRAM Tier Breakdown and Performance Observations

The developer conducted testing by mapping specific model requirements against different hardware tiers to facilitate automatic hardware detection for the Lemonade application. According to the report, the performance and utility of AI models vary significantly based on available memory:

  • CPU-Only Configurations: Systems without a dedicated GPU were found to be capable of running small chat models alongside file search functions. While described as “usable,” the developer noted the performance is not fast. The disk footprint for such a setup was approximately 2.6GB.
  • 8GB VRAM: This tier was identified as the point where AI performance “stops feeling like a compromise,” capable of running chat, image processing, and tools within a single model, totaling 5.1GB.
  • 10GB to 12GB VRAM: This configuration allows for a dedicated vision model to run alongside a chat model, with a total memory footprint of 11.1GB.
  • 24GB VRAM: High-end configurations allow for large models, vision capabilities, and code processing to remain resident in memory simultaneously, totaling 20.6GB.

The 10GB VRAM and 32GB RAM Configuration

One of the more notable findings in the report involves a hybrid configuration consisting of 10GB of VRAM paired with 32GB of system RAM. The developer reported that this setup successfully runs a 35B sparse model using full quantized weights without distillation.

While the model requires approximately 27GB of disk space, the developer cautioned that the execution speed is low. However, the results suggest that older hardware, such as the NVIDIA GeForce RTX 3080, may remain viable for running larger models through tiered memory usage rather than being rendered obsolete by increasing model sizes.

Strategic Implications for Model Management

The testing led the developer to conclude that model swapping—unloading one model to make room for another—may be more effective for performance than attempting to run a single, mediocre model that attempts to perform multiple tasks simultaneously.

The report also noted a lack of comprehensive data regarding AMD hardware, with the developer stating that testing for AMD cards remains “thinner” than desired. While the findings provide a granular look at local AI hardware requirements, they remain unverified by independent third-party benchmarks.