According to @hyperbolic_labs, UC Berkeley Sky Computing Lab researcher Sijun Tan built LLoCO, a technique that processes 128k context while using 30x fewer tokens. source: Hyperbolic Labs on X It delivers 7.62x faster inference in their reported case study. source: Hyperbolic Labs on X The project was powered by Hyperbolic Labs' NVIDIA H100 GPUs. source: Hyperbolic Labs on X

According to Hyperbolic (@hyperbolic_labs), LLoCO on NVIDIA H100 delivered up to 7.62x faster inference on 128k-token sequences and 11.52x higher throughput during finetuning, and enabled processing of 128k tokens on a single H100 (source: Hyperbolic on X, Aug 21, 2025). For trading context, these stated gains are concrete performance datapoints for assessing throughput per H100 in long-context LLM workloads and may inform evaluation of AI compute efficiency tied to H100 deployments (source: Hyperbolic on X, Aug 21, 2025).

According to @hyperbolic_labs, LLoCO outperformed baseline methods across all tested datasets, matched 32k-context models while using 30× fewer tokens, and delivered a +13.64 score improvement over non-finetuned compression (source: @hyperbolic_labs on X, Aug 21, 2025). Because major LLM APIs charge per token, a 30× token reduction at parity performance directly lowers token usage for the same task, a key efficiency metric for cost-sensitive AI workloads (source: OpenAI Pricing). These quantified results provide concrete benchmarks traders can use to compare long-context compression approaches and assess efficiency trends relevant to AI-linked crypto and compute markets (source: @hyperbolic_labs on X, Aug 21, 2025).

According to @hyperbolic_labs, LLoCO first compresses long contexts offline, then applies domain-specific LoRA fine-tuning, and finally serves compressed embeddings for inference while maintaining compatibility with standard RAG pipelines, source: @hyperbolic_labs on X, Aug 21, 2025. No token, performance metrics, or crypto integration details are disclosed in the source, source: @hyperbolic_labs on X, Aug 21, 2025.

According to Hyperbolic, LLoCO delivered up to 7.62x faster inference on 128k-token sequences on Nvidia H100 GPUs, based on their reported results; source: Hyperbolic @hyperbolic_labs, Aug 20, 2025. According to Hyperbolic, LLoCO achieved 11.52x higher throughput during finetuning on H100; source: Hyperbolic @hyperbolic_labs, Aug 20, 2025. According to Hyperbolic, LLoCO enabled processing of 128k tokens on a single H100; source: Hyperbolic @hyperbolic_labs, Aug 20, 2025.

According to @hyperbolic_labs, LLoCO outperformed baseline methods across all tested datasets (source: @hyperbolic_labs, Aug 20, 2025). According to @hyperbolic_labs, it matched 32k context models while using 30× fewer tokens (source: @hyperbolic_labs, Aug 20, 2025). According to @hyperbolic_labs, it achieved a +13.64 score improvement over non-finetuned compression (source: @hyperbolic_labs, Aug 20, 2025). The post did not include details on cryptocurrencies or market impact (source: @hyperbolic_labs, Aug 20, 2025).