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Distributed GPU Sharing for Sustainable AI Training

Unify heterogeneous GPUs (2GB–32GB+) into a single compute pool. Train AI models at lower cost with measurable sustainability impact.

Why Choose Hugin?

Democratizing AI infrastructure with a community-powered compute grid.

💸

Cost Efficiency

Access GPU compute at 30-50% lower cost than centralized cloud providers. Our distributed network eliminates data center overhead, passing savings directly to you.

Faster Iteration

No more waiting in queues for H100s. Launch jobs instantly across thousands of consumer and pro-sumer GPUs perfectly suited for fine-tuning and inference.

📈

Flexible Scaling

From single-GPU prototypes to multi-shard distributed training. Seamlessly scale your workload across a heterogeneous grid of NVIDIA, AMD, and Intel hardware.

How Hugin Works

Six-step pipeline from job definition to verified delivery with transparent billing.

01
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Job Definition

Data classification, target metric, and pool selection.

02
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Cost Estimate

HU & GPU-seconds estimate with upper bound and SLA.

03
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Sharding & Scheduling

Capability-aware distribution, node selection, redundant execution.

04
⚙️

Execution

Sandboxed micro-shard execution with telemetry, health checks, auto-retry.

05

Aggregation & Verification

Quality threshold, spot-check, and anomaly detection validation.

06
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Billing & Payment

Verified job billing, automatic GPU owner payout.

Trusted Pools

Choose the right trust level for your workload — from open community to dedicated enterprise capacity.

Community

Best-effort execution for non-sensitive workloads. Open participation from all verified node agents.

  • Open to all GPU owners
  • Best-effort SLA
  • Public/non-sensitive data only
  • Lowest cost tier
Verified

KYC/KYB verified nodes with policy enforcement, optional EU/EEA geo-restrictions, and secure boot signals.

  • KYC & KYB verified
  • Policy enforcement
  • EU/EEA geo-restriction
  • Secure boot & TPM
  • Higher completion targets
Dedicated

Customer-reserved capacity with custom policies. Maximum predictability and control for enterprise workloads.

  • Reserved capacity
  • Custom policies
  • Highest predictability
  • Customer-specific allowlist
  • Enterprise SLA

Transparent HU-Based Pricing

HU (Hugin Unit) is your simple billing unit. 1 HU ≈ 3,600 normalized GPU-seconds. Pre-estimate and upper bound before every job.

0.21EUR / HU
Transparent Pricing for Training
  • More iterations with same budget
  • Transparent HU + GPU-seconds billing
  • Pre-estimate & upper bound guaranteed
  • Community, Verified, or Dedicated pools
  • ESG reporting: kWh/HU & CO₂e/HU
Start Training Now
Share Your Compute
Turn idle devices into passive value. Hugin network harnesses compute power from any connected device globally.
  • 📱 Smartphones (Browser or App)
  • 🚗 Electric Vehicles (EV Compute)
  • 🌐 Any Web Browser Tab
  • 💻 Desktop & Server GPUs
Join the Network

HU Rate by Device Class

Owner Payout Rate: 1 HU = €0.153 net  ·  73% owner share  ·  ~20% above RunPod / Vast.ai

Device Class~HU/HourExamples
Smartphones (Idle)0.05 - 0.15iPhone 15 Pro, Pixel 8
Electric Vehicles (EV)0.2 - 0.4Tesla MCU, Polestar 2
4-6GB Consumer0.4 - 0.6GTX 1650, RTX 3050
8-12GB Consumer0.6 - 0.9RTX 3060, RTX 4070
16-24GB Workstation1.6 - 2.8RTX 3090, RTX 4090
24-48GB+ Datacenter2.8 - 4.5A100, H100

Frequently Asked Questions

Everything you need to know about training AI models on Huginn.

What is Project Huginn?+
Project Huginn is a distributed GPU sharing platform that lets you train and fine-tune AI models at up to 50% lower cost than traditional cloud providers. It pools heterogeneous GPUs (from RTX 3080 to H100) into a unified compute grid. Built by SMware ApS, a Startup Denmark approved company.
How much does it cost to train an AI model on Huginn?+
Huginn uses Hugin Units (HU) for billing. 1 HU = €0.21. A typical LoRA fine-tuning of a 7B model with 1,000 training examples costs approximately 5-8 HU (€1-2). Costs are estimated before you start, with an upper bound guarantee — no surprise charges.
What AI models can I fine-tune?+
Huginn provides 7 base models for free: LLaMA 3.1 (8B & 70B), Mistral 7B, Phi-3 Mini 3.8B, CodeLlama 7B, Gemma 2 9B, and Qwen 2.5 7B. You select a base model and upload your own training data — Huginn handles the rest.
What training methods does Huginn support?+
Three methods: LoRA (Low-Rank Adaptation) for fast, efficient training with ~12GB VRAM; QLoRA (4-bit quantized) for even lower memory usage with ~8GB VRAM; and Full Fine-Tune for highest quality with ~40GB+ VRAM. LoRA with rank 16 is recommended for most use cases.
What data formats are supported for training?+
JSONL (Alpaca format with instruction/input/output fields), CSV (with instruction, input, output columns), and Chat Format JSONL (with messages array). Maximum upload size is 500MB. We recommend at least 1,000 examples for good results.
How is Huginn different from other GPU cloud platforms?+
Unlike traditional GPU rental platforms that only offer raw compute, Huginn provides an all-in-one AI training experience. You get a built-in Model Studio with 7 free base models, an integrated fine-tuning pipeline, automatic cost estimation with upper bounds, and a ChatGPT-style playground to test your model instantly — no setup, no configuration, just upload your data and start training.
Can I earn money by sharing my device?+
Yes! Huginn lets you earn from virtually any device. Share your desktop GPU, laptop, tablet, smartphone, or even your electric vehicle's compute power while you drive. Connect via the Huginn Agent app or simply open a browser tab. Every connected device contributes to the network and earns you HU. Desktop GPUs earn the most (up to €0.60/hour for high-end cards), but even a smartphone or browser tab generates passive income 24/7.
How do I share my GPU and start earning?+
It takes under 5 minutes: 1) Sign in with Google and choose GPU Owner role, 2) Go to GPU Agent page in your dashboard, 3) Download the agent file and generate a token, 4) Run the command in your terminal. The agent auto-detects your GPU and starts processing jobs. You can also use the browser-based Web Engine with zero downloads. Read our full guide at projecthuginn.com/how-to-share for step-by-step instructions.
Is my training data secure?+
Yes. All training jobs run in sandboxed environments with complete data isolation. Each user's models and datasets are private. We use session-based authentication and encrypted connections for all data transfers.
Can I download my trained model?+
Yes. After training completes, you can download your fine-tuned model in .safetensors format. You can run it locally with Ollama, llama.cpp, vLLM, or HuggingFace Transformers. You can also keep it on Huginn and test it via the built-in playground.
What GPU tiers are available?+
Five tiers: T1 (H100/A100 80GB+, 4.50 HU/hr), T2 (A100 40GB/RTX 4090, 2.80 HU/hr), T3 (RTX 4080/3090, 1.60 HU/hr), T4 (RTX 3080/2080 Ti, 0.90 HU/hr), and T5 (entry-level, 0.40 HU/hr). Higher tiers are faster but cost more per hour.