HYPHA
A practical, low cost path to distributed autonomy. Request to read one of the White Papers below to learn more about HYPHA.
HYPHA Distributed Compute Engine
Summary: HYPHA is a distributed compute engine that dispatches workloads as micro-operations across autonomous agents. It processes data entirely in memory and writes only final structured output to disk. Agents pull work independently with no orchestration layer, no scheduler overhead, and no dependency on cloud infrastructure. The results demonstrate that HYPHA delivers production-grade distributed compute on commodity hardware. Benchmarked against the Human Connectome Project Young Adult dataset.
HYPHA COBOL Batch Offload
Summary: This paper documents Hypha distributed transformer inference v0 on commodity x86 CPU systems. The experiment partitions one Qwen2.5-7B-Instruct transformer across physical machines rather than sending independent prompts to separate models
Distributed Transformer Inference
Summary: This paper documents Hypha distributed transformer inference v0 on commodity x86 CPU systems. The experiment partitions one Qwen2.5-7B-Instruct transformer across physical machines rather than sending independent prompts to separate models.
Distributed Inference Across Heterogeneous Commodity Hardware
Summary: Hypha distributed inference v0 was built to test a simple but consequential question: can one transformer execute as a single inference path across physically separate, heterogeneous machines rather than being confined to one server or one accelerator? The answer from the current prototype is yes, and it is not multi-agent prompting, model voting or two models exchanging text.
Distributed Transformer Inference
Summary: This paper documents Hypha distributed transformer inference v0 on commodity x86 CPU systems. The experiment partitions one Qwen2.5-7B-Instruct transformer across physical machines rather than sending independent prompts to separate models.
Resilient Heterogenous Distributed Inference
Summary: A repeated, matched 12/12 pipeline split averaged 36.820 tokens/s versus 21.509 tokens/s for the A6000-only control, a 71.2% throughput increase. A separate forced-failure experiment then killed the RTX 3060 inference rank with SIGKILL; Hypha detected and contained the failure, reclaimed resources, rebuilt the 12/12 pipeline, retried inference, and completed a 1,024-token response without operator intervention after the failure was detected.
Hypha Secure Remote Compute Fabric
Summary: Hypha treats network reachability, cryptographic identity, authorization, execution capability, and control-plane role as distinct security concerns. This separation allows geographically distributed and heterogeneous systems to participate in one governed compute fabric without equating network presence with trust.
Hypha Gemma 3 27B BF16 Distributed Capacity Demonstration
Summary: Hypha executed the original 51.10 GiB Gemma 3 27B checkpoint across two GPUs even though the checkpoint exceeds the VRAM of either card independently. The final run preserved BF16 weights, disabled CPU offload, used a verified 53 / 9 layer partition, generated a natural stop, and completed clean Hypha lifecycle recovery.
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