Local LLM Hardware Statistics (2026)
As of November 11, 2026, BenchLM documents license, quantization, and reference hardware for 67 ranked open-weight models.
Key statistics
- As of November 11, 2026, BenchLM documents license, quantization, and reference hardware for 67 ranked open-weight models.
- As of November 11, 2026, 20 of the 67 ranked local-model receipts on BenchLM use one 24GB NVIDIA RTX 4090 as the reference configuration.
- As of November 11, 2026, InternScience's Sage-A1.5 is the highest-scoring model in BenchLM's documented single-GPU subset at 62.6/100, with a 24GB RTX 4090 reference setup.
- As of November 11, 2026, 41 of 67 ranked models in BenchLM's deployment subset (61%) use an OSI-approved license.
As of November 11, 2026, BenchLM documents license, quantization, and reference hardware for 67 ranked open-weight models.
67 models
As of November 11, 2026, 20 of the 67 ranked local-model receipts on BenchLM use one 24GB NVIDIA RTX 4090 as the reference configuration.
20 models
Methodology & sources
This page joins the current ranked open-weight cohort to BenchLM's reviewed self-host catalog, last checked 2026-11-04. A “single-GPU” row has a documented reference configuration using at most 32GB of GPU or unified memory. These receipts are deployment examples, not a claim that every runtime, quantization, or context length will fit.
Cite these statistics
Every number on this page is generated from BenchLM's live dataset and refreshed with each data update. Link any statistic directly using its anchor, or cite the page as:
BenchLM.ai, "LLM Statistics" (November 11, 2026), https://benchlm.ai/stats/local-llm-hardwareQuestions
How many ranked local LLMs fit on one 24GB GPU?
As of November 11, 2026, 20 of the 67 ranked models with reviewed deployment receipts use one 24GB RTX 4090 as their reference setup. This count covers the documented quantization and configuration only; longer contexts and different runtimes can require more memory.
What is the highest-scoring LLM documented for one GPU?
As of November 11, 2026, InternScience's Sage-A1.5 leads BenchLM's documented single-GPU subset at 62.6/100. Its reference row uses one 24GB RTX 4090. The score measures benchmark capability, not local throughput, latency, or the quality loss from a specific quantization.
Does open-weight mean open source?
No. As of November 11, 2026, 41 of 67 ranked models in BenchLM's deployment subset use an OSI-approved license. “Open-weight” only means the weights are downloadable; community and custom licenses can restrict commercial use, redistribution, or derived models.
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