324 GPUs. 18 terabytes of VRAM. 100 edge nodes. Single-tenant, sovereign compute for AI training, fine-tuning, and inference. Your data never leaves your rack.
324
Total GPUs
18 TB
Total VRAM
100
Edge Nodes
0
Cloud Dependencies
Fleet Composition
| GPU | Architecture | VRAM | Units | Total VRAM |
|---|---|---|---|---|
| NVIDIA RTX 6000 Pro | Blackwell | 96 GB | 128 | 12,288 GB |
| NVIDIA RTX 4500 | Blackwell | 32 GB | 48 | 1,536 GB |
| NVIDIA RTX 4000 | Blackwell | 24 GB | 48 | 1,152 GB |
| BeeMini Edge Nodes | CPU (Intel) | — | 100 | — |
| Total Fleet | 324 + 100 | 14,976 GB |
GPU Fleet
128
Units
96 GB
VRAM Each
12.3 TB
Total VRAM
Blackwell
Architecture
The backbone. 96GB VRAM per card runs 70B+ models unquantized. Multi-GPU training for 200B+ parameter models. The same silicon that powers our CoVe verification pipeline — 235B parameter model verification at production scale.
48
Units
32 GB
VRAM Each
1.5 TB
Total VRAM
Blackwell
Architecture
The workhorse tier. 32GB handles 7B–13B models at full precision, 34B quantized. Ideal for fine-tuning, LoRA training, batch evaluation, and high-throughput inference. Cost-effective for workloads that don't need 96GB.
48
Units
24 GB
VRAM Each
1.2 TB
Total VRAM
Blackwell
Architecture
Edge GPU tier. 24GB runs specialized 7B models at full speed, quantized 13B models, and vision-language pipelines. Low power draw, high density. Perfect for always-on inference, model serving, and edge deployment.
Edge Network
100
BeeMini Nodes
Ultra-low-power CPU nodes for orchestration, routing, and lightweight inference
~6W
Per Node
Intel-based, 10Gbps networking, sovereign edge processing at pennies per day
24/7
Always On
Fleet watchdog, pipeline orchestration, health monitoring, data routing
The BeeMini fleet handles everything that doesn't need a GPU — pipeline orchestration, data staging, CoVe workflow management, fleet health monitoring, and edge inference for small models. Foremen don't need muscle. They need to be everywhere, always on, directing traffic.
What You Can Run
Model Training
Full pre-training and continued pre-training on multi-GPU clusters. 96GB per card means no compromise on batch size or sequence length. Train 7B to 200B+ parameter models.
Fine-Tuning
LoRA, QLoRA, and full fine-tuning on any open-weight model. Qwen, Llama, Mistral, Gemma, Phi — bring your base model and your dataset. We return weights.
Production Inference
Serve your models at scale with vLLM, TGI, or llama.cpp. Dedicated GPU allocation, no noisy neighbors, predictable latency. API endpoints on your terms.
Sovereign AI
For regulated industries — healthcare, legal, financial services. Your data stays on-premises. No cloud provider has access. Full audit trail. HIPAA-ready architecture.
Batch Processing
Large-scale dataset processing, embedding generation, evaluation runs, CoVe verification pipelines. Burst capacity when you need it, return GPUs when you don't.
Edge Deployment
Deploy specialized models to edge GPU nodes. Low-latency inference at the point of need. Clinic, office, factory floor — wherever the last mile is.
Why Sovereign
No Cloud Egress
Your data never traverses a public cloud. No egress fees. No third-party data processing agreements. No vendor lock-in.
Single Tenant
Your GPU is your GPU. No shared resources, no noisy neighbors, no performance variability. Dedicated hardware, dedicated to you.
Predictable Cost
Flat monthly pricing. No per-token charges, no surprise bills, no metered bandwidth. Know exactly what you'll pay before you commit.
Data Sovereignty
Critical for healthcare, legal, and financial AI. Your training data, your model weights, your inference logs — all stay on hardware you control.
Full Stack Control
Choose your framework, your serving stack, your model. vLLM, TGI, Unsloth, Axolotl — run whatever you need. Root access available.
Scale on Demand
Start with 1 GPU. Scale to 128. Add edge nodes. Build multi-node training clusters. Infrastructure grows with your workload.
Pricing
Inference
$49.99
10 hours
Dedicated GPU inference. RTX 6000 Pro 96GB. API access.
Fine-Tune 7B
$299
per job
We fine-tune your 7B model. LoRA + merged weights delivered.
Fine-Tune 70B
$999
per job
Multi-GPU fine-tuning. 70B+ class models. Full precision.
Dedicated GPU
$2,499
per month
RTX 6000 Pro 96GB. Single tenant. 24/7. Cancel anytime.
Custom clusters (4–128 GPUs) available. Contact us for enterprise pricing.