According to @hyperbolic_labs, the GAUSS benchmark has been released to evaluate LLMs by highlighting strengths and weaknesses across different math skills, with contributions from researchers at CalTech, UC Berkeley, Stanford, NVIDIA, University of Washington, and University of HK, and materials available at gaussmath.ai; source: Hyperbolic on X, 2025-09-12, x.com/hyperbolic_labs/status/1966592079745667352; gaussmath.ai. No token mentions, model rankings, or market impacts were disclosed in the announcement; source: Hyperbolic on X, 2025-09-12, x.com/hyperbolic_labs/status/1966592079745667352. For AI-crypto due diligence, traders can reference GAUSS-linked disclosures when teams cite math reasoning capability and verify details via the public site gaussmath.ai; source: Hyperbolic on X, 2025-09-12, x.com/hyperbolic_labs/status/1966592079745667352; gaussmath.ai. Related AI infrastructure tokens often tracked by the market include RNDR for GPU rendering, TAO for decentralized machine learning, and AKT for decentralized compute marketplaces; sources: rendernetwork.com; bittensor.com; akash.network.

According to @hyperbolic_labs, Hyperbolic has launched Qwen3-Next in BF16 featuring an 80B-parameter Mixture-of-Experts with only 3B active parameters. Source: @hyperbolic_labs on X, Sep 11, 2025. The release states training is about 10x cheaper versus Qwen3-32B and inference throughput is roughly 10x higher on sequences longer than 32K tokens. Source: @hyperbolic_labs on X, Sep 11, 2025. Hyperbolic also says it is the first to serve Qwen3-Next on Hugging Face and highlights a partnership with Alibaba Qwen. Source: @hyperbolic_labs on X, Sep 11, 2025. The source does not mention any cryptocurrency or token integrations, indicating no direct on-chain catalyst disclosed in this announcement. Source: @hyperbolic_labs on X, Sep 11, 2025.

According to Hyperbolic, its AI GPU pricing is set at 2.65 dollars per hour, which the company states is up to four times cheaper than major cloud providers, providing a clear benchmark for compute cost comparisons in AI infrastructure trades (source: Hyperbolic on X, Sep 2, 2025). According to Hyperbolic, most hyperscalers charge over 10 dollars per GPU hour and require full 8-GPU bundles, giving traders explicit reference points for evaluating cost differentials across AI compute vendors (source: Hyperbolic on X, Sep 2, 2025).

According to @hyperbolic_labs, H200 access for AI workloads is available to start experimenting today, and the team is taking GPU reservations and bulk requests via its website and direct contact (source: Hyperbolic on X, Sep 2, 2025). For traders, this constitutes a verified supply-side update from a GPU provider that can be used to track AI compute availability relevant to crypto infrastructure strategies, as communicated by @hyperbolic_labs (source: Hyperbolic on X, Sep 2, 2025).

According to @hyperbolic_labs, Hyperbolic announced that H200 GPUs are available to start experimenting now via the link in its post and that the team is accepting GPU reservations and bulk requests for AI workloads (Source: Hyperbolic (@hyperbolic_labs) on X, Sep 2, 2025). The post does not provide pricing, delivery timelines, capacity allocations, or any mention of cryptocurrencies or blockchain integrations, indicating only a channel update for H200 access (Source: Hyperbolic (@hyperbolic_labs) on X, Sep 2, 2025).

According to @hyperbolic_labs, Hyperbolic announced a partnership with the ARC Prize, describing it as a competition pushing the frontiers of AGI, and stated that recipients can receive up to $1,000 in compute credits; source: Hyperbolic on X (Aug 28, 2025). The announcement did not reference any cryptocurrency, token, blockchain integration, or on-chain component; source: Hyperbolic on X (Aug 28, 2025).

According to @hyperbolic_labs, the company is providing ARC Prize participants with priority access to high-performance GPU clusters so researchers can train and test complex models without hardware limitations; source: @hyperbolic_labs on X, Aug 28, 2025. The post does not specify GPU type, cluster size, pricing, or timeframes, leaving no quantifiable metrics for immediate valuation or capacity analysis; source: @hyperbolic_labs on X, Aug 28, 2025. The announcement includes no token, stock, or partnership details, providing no direct trading catalyst in the post; source: @hyperbolic_labs on X, Aug 28, 2025.

According to @hyperbolic_labs, its H100 systems sustained the most demanding workloads for a full week with no interruptions during massive parameter optimization runs, delivering consistent performance from start to finish, source: @hyperbolic_labs on X, Aug 21, 2025. For traders, the key datapoints are seven days of continuous operation, zero interruptions reported, and consistency under heavy optimization workloads, evidencing operational stability as presented, source: @hyperbolic_labs on X, Aug 21, 2025. No throughput, latency, cost, or power metrics were disclosed in the post, limiting direct performance-per-dollar comparisons at this time, source: @hyperbolic_labs on X, Aug 21, 2025.

According to @hyperbolic_labs, the company directed users to a full case study on how its GPU infrastructure can accelerate AI research; source: Hyperbolic @hyperbolic_labs on X, Aug 21, 2025. The post does not disclose performance metrics such as throughput, cost per training run, cluster scale, or client benchmarks, leaving no immediate quantitative catalyst for trading models; source: Hyperbolic @hyperbolic_labs on X, Aug 21, 2025. For trading, monitor the linked case study for verifiable figures that could inform compute cost assumptions and valuations in AI infrastructure and related crypto narratives, noting that the tweet alone provides no new data; source: Hyperbolic @hyperbolic_labs on X, Aug 21, 2025.

According to @bolsaverse, only 9 hours remain to vote for the next project on Kaito Connect, with a significant preference shown for Hyperbolic. The user allocated 90% of their vote to Hyperbolic, indicating its potential to win despite skepticism about Bitcoin DeFi projects like Lombard. The commentary suggests that Layer 2 solutions on Bitcoin are perceived as similar to existing EVM (Ethereum Virtual Machine) implementations.