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Epoch AI consumer GPU analysis
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Epoch AI
Relevant to AI safety discussions about the democratization of frontier capabilities, accessibility of powerful models outside institutional settings, and implications for governance and deployment risk timelines.
Metadata
Importance: 52/100organizational reportanalysis
Summary
Epoch AI analyzes how consumer GPUs like the RTX 5090 can run open-weight models that match frontier LLM performance from 6-12 months prior. The analysis tracks this gap across multiple benchmarks (GPQA Diamond, MMLU-Pro, LMArena) and finds the democratization trend is driven by open-weight scaling, model distillation, and GPU progress.
Key Points
- •A single RTX 5090 (~$2500) can run models matching frontier LLM performance from 6-12 months ago, consistent with prior Epoch estimates of a 5-22 month open-model lag.
- •The gap is measured across four benchmarks: GPQA Diamond (7 months), MMLU-Pro (7 months), Artificial Analysis Intelligence Index (6 months), and LMArena (12 months).
- •Small open models may be benchmark-optimized, so real-world capability lag could be somewhat longer than these figures suggest.
- •Key drivers of narrowing gap include open-weight scaling rates approaching closed-source, model distillation techniques, and consumer GPU improvements enabling larger models at home.
- •The RTX 5090 era supports models up to ~40B parameters, up from ~28B for the RTX 4090 era, enabling locally runnable models like Qwen3 32B and EXAONE 4.0 32B.
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| AI Capability Threshold Model | Analysis | 72.0 |
| Structured Access / API-Only | Approach | 91.0 |
Cached Content Preview
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Using a single top-of-the-line gaming GPU like NVIDIA’s RTX 5090 (under $2500), anyone can locally run models matching the absolute frontier of LLM performance from just 6 to 12 months ago. This lag is consistent with our previous estimate of a [5 to 22 month gap for open-weight models of any size](https://epoch.ai/blog/open-models-report#open-models-have-lagged-on-benchmarks-by-5-to-22-months). However, it should be noted that small open models are more likely to be optimized for specific benchmarks, so the “real-world” lag may be somewhat longer.
Benchmark
GPQA DiamondMMLU-ProAA IntelligenceLMArena
Models that fit on a single consumer GPU trail the absolute frontier by less than a year.
7 monthsRTX 5090 Era(≤ 40B models)RTX 4090 Era(≤ 28B models)Qwen3 32BEXAONE 4.0 32BGrok 4o1-mini (high)GPT-4oPhi 3Phi 4GPT-4Mistral 7BJuly 2023Oct. 2023Jan. 2024Apr. 2024July 2024Oct. 2024Jan. 2025Apr. 2025July 20250%20%40%60%80%100%Release dateGPQA-Diamond accuracyFrontier modelsTop-1 modelsOther modelsOpen models on aconsumer GPUTop-1 modelsOther models
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Models that fit on a single consumer GPU trail the absolute frontier by less than a year.
7 monthsRTX5090Era(≤40Bmodels)RTX 4090 Era(≤ 28B models)Gemma 2 27BGPT-4oQwQ-32BGrok 4o1EXAONE 4.0 32BClaude 2Mistral 7BJuly 2023Oct. 2023Jan. 2024Apr. 2024July 2024Oct. 2024Jan. 2025Apr. 2025July 20250%20%40%60%80%100%Release dateMMLU-Pro accuracyFrontier modelsTop-1 modelsOther modelsOpen models on a consumer GPUTop-1 modelsOther models
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Models that fit on a single consumer GPU trail the absolute frontier by less than a year.
6 monthsRTX5090Era(≤40Bmodels)RTX 4090 Era(≤ 28B models)Gemma 2 27BQwQ-32BQwen3 30B-A3BGrok 4o1GPT-4oClaude 2Qwen-14BJuly 2023Oct. 2023Jan. 2024Apr. 2024July 2024Oct. 2024Jan. 2025Apr. 2025July 2025020406080100Release dateArtificial Analysis Intelligence IndexFrontier modelsTop-1 modelsOther modelsOpen models on a consumer GPUTop-1 modelsOther models
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Models that fit on a single consumer GPU trail the absolute frontier by less than a year.
12 monthsRTX5090Era(≤40Bmodels)RTX 4090 Era(≤ 28B models)Vicuna-13BGemma 2 27BQwQ-32BQwen3 30B-A3BGPT-4oo1GPT-5July 2023Oct. 2023Jan. 2024Apr. 2024July 2024Oct. 2024Jan. 2025Apr. 2025July 202511001200130014001500Release dateLMArena scoreFrontier modelsTop-1 modelsOther modelsOpen models on a consumer GPUTop-1 modelsOther models
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Several factors drive this democratizing trend, including a [comparable rate of scaling among open-weight models to the closed-source frontier](https://epoch.ai/blog/open-models-report#the-scaling-of-open-models-lags-by-about-15-months), the success of te
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