Hydrogen-1.0

Hydrogen-1.0 is an open-weight, 753-billion-parameter mixture-of-experts (MoE) scientific foundation model released by Hangzhou-based Deep Principle. Built by post-training Z.ai's GLM-5.3 on chemistry, materials science, and life sciences corpora, the model incorporates real-world laboratory records and autonomous agent discovery trajectories. Released under the permissive MIT license, Hydrogen-1.0 gives research teams and machine learning engineers an unencumbered, inspectable alternative to proprietary frontier models for high-complexity scientific discovery.

Unlike standard generalist LLMs, Hydrogen-1.0 is trained specifically to bridge the gap between scientific hypothesis generation and experimental validation. The model activates roughly 40 billion parameters per forward pass out of its 753-billion total footprint, pairing the deep capacity of a mega-scale MoE with practical inference speeds. Deep Principle provides no hosted cloud API or pricing tiers: the release exists strictly as open weights for local or private-cloud infrastructure, targeting labs that require full data privacy and zero vendor lock-in.

At 753B parameters, running this model on-premise requires serious hardware planning. However, native low-bit quantization targets and an architecture tailored for multi-GPU serving allow organizations with modern accelerators to run Hydrogen-1.0 locally for high-throughput laboratory automation, research synthesis, and scientific computing.

Hydrogen-1.0 relies on a sparse Mixture-of-Experts architecture containing 753B total parameters, of which roughly 40B are active during inference. This design provides significant Hydrogen-1.0 MoE efficiency: the computational cost and latency per token mirror that of a mid-sized dense model, while the 753B total parameter space retains the vast knowledge base required for interdisciplinary scientific domains.

The model boasts a native 1,000,000-token context window with support for up to 128,000 generation tokens. This context budget allows engineers to feed full academic papers, extensive reaction databases, clinical trial records, and complex patent documentation into a single prompt without chunking or external retrieval degradation.

Deep Principle published the model in three official formats:

• BF16 (Unquantized): Full-precision base weights requiring over 1.5 TB of raw VRAM just to store the parameter tensors.
• NVFP4: 4-bit floating point builds optimized for NVIDIA Blackwell architectures (B200 and B300) leveraging native microscopic scaling.
• MXFP4: Microscopic 4-bit floating point builds designed for AMD Instinct accelerators (MI350X and MI355X).

Inference is validated out-of-the-box using SGLang across an 8-GPU node configuration, taking advantage of expert parallelism and tensor parallelism to manage the massive parameter distribution.

Hydrogen-1.0 handles standard text modalities including chat, math, multi-step reasoning, and instruction-following, but its post-training prioritizes domain-specific scientific problem solving. On formal benchmarks, Deep Principle reports a Hydrogen-1.0 reasoning benchmark score of 93.0% on GPQA-Diamond and 38.3% on FrontierScience-Research.

Key deployment use cases for local engineering teams include:

• Chemical Reaction Network Exploration: Navigating non-templated synthetic pathways, mapping complex reaction graphs, and predicting reaction mechanisms without predefined heuristics.
• Spectroscopic and Structural Elucidation: Analyzing and cross-referencing multi-dimensional NMR, mass spectrometry, and crystallographic data against molecular candidates.
• Materials Property Prediction: Formulating candidate structures for specific electronic, thermal, or mechanical performance criteria, drawing on underlying physics and crystal field theories.
• Laboratory Agent Planning: Ingesting raw experimental protocols, identifying procedural failure points, and constructing executable scripts for automated liquid handlers and synthesis hardware.

Because the model supports 128K output tokens, it can generate exhaustive mathematical proofs, large-scale synthetic datasets, and complete computational biology pipelines in a single uninterrupted generation.

Deploying a local AI model with 753B parameters in 2026 requires understanding the difference between memory footprint and active compute. While inference compute tracks the 40B active parameters, your memory subsystem must house all 753B parameters simultaneously.

The table below breaks down the minimum memory footprint across common precision levels:

If you are wondering how to run 753B model on consumer GPU setups: you cannot run it on standard desktop hardware. A single NVIDIA RTX 4090 (24 GB) or workstation RTX 6000 Ada (48 GB) lacks the capacity for even the layer overhead of this model. Even a 4-way RTX 4090 cluster provides only 96 GB of VRAM, which falls well short of the ~380 GB needed for a 4-bit quantized model.

Realistic local deployment targets include:

• Best GPU for Hydrogen-1.0: An 8-GPU node of NVIDIA B200 (192 GB each) or AMD Instinct MI350X (288 GB each) provides optimal memory bandwidth and native support for NVFP4/MXFP4.
• Existing Enterprise Clusters: 8x NVIDIA H100/H200 (80 GB or 141 GB) running FP8 or Q4 quantization via tensor parallelism.
• Unified Memory Workstations: Multi-system Apple Silicon setups (such as networked M4 Max/Ultra machines running unified RAM pools) or multi-socket EPYC/Xeon servers with 1 TB to 1.5 TB of high-bandwidth DDR5 RAM using CPU offloading.

For production datacenters, the native NVFP4 and MXFP4 weights provide the cleanest accuracy retention and hardware-level throughput. If you are serving the model on custom infrastructure or mixed nodes, the best quantization for Hydrogen-1.0 in standard runtimes is Q4_K_M. A 4-bit quantization keeps total weight size near 380 GB, enabling deployment across eight 64 GB/80 GB cards while leaving headroom for KV cache growth.

Because only 40B parameters are active per token, Hydrogen-1.0 tokens per second are significantly higher than what a dense 753B model would produce:

• 8x Blackwell (B200 NVFP4 with SGLang): Expect 35-50 tokens/second for single-stream generation, scaling efficiently under concurrent batches.
• 8x Hopper (H100 80GB with FP4/Q4): Typical throughput sits around 15-25 tokens/second.
• System RAM / CPU Offloading (1.5 TB DDR5): Throughput drops dramatically to roughly 0.8-2.5 tokens/second due to system memory bandwidth bottlenecks.

For quick evaluation, local execution via frameworks like Ollama or llama.cpp using GGUF builds is the fastest way to get started, provided your host system has sufficient memory mapped across unified banks. For high-concurrency production research environments, deploy the official weights using SGLang with expert parallelism enabled.

Evaluating Hydrogen-1.0 requires comparing it against other open-weight models occupying similar parameter or performance tiers:

• Hydrogen-1.0 vs. DeepSeek-V3 / DeepSeek-R1: DeepSeek-V3 uses an MoE layout with 671B total and 37B active parameters. While DeepSeek models provide superior generalist software engineering and everyday conversational performance, Hydrogen-1.0 dramatically outperforms them in chemistry, materials science, and experimental design tasks. DeepSeek requires roughly 340-700 GB of memory depending on quantization, putting it in a very similar hardware tier to Hydrogen-1.0.
• Hydrogen-1.0 vs. Llama 3.1 405B: Meta's Llama 3.1 405B is a dense model. Although Llama 405B requires less raw VRAM at 4-bit precision (~230 GB vs. ~380 GB for Hydrogen-1.0), its compute demands are much heavier: every token must calculate all 405 billion parameters. Hydrogen-1.0 processes tokens faster on compatible memory architectures because it computes only 40B active parameters per step, while offering a context window of 1M tokens compared to Llama's 128K.

If your objective is building a local general-purpose software agent, generalist models remain easier to manage. If your team is running autonomous laboratory workflows, evaluating complex scientific papers, or modeling reaction mechanisms on local infrastructure, Hydrogen-1.0 is currently the most capable open-weight option available.