Hardware Specifications
Technical specifications and architecture details for NeuralChip products
Product Line
NeuralChip NC-1000
Development and small-scale inference
Entry Level
Process
7nm FinFET
Cores
16 AI Cores
Memory
32GB HBM2
Bandwidth
1.2 TB/s
Power
150W TDP
Performance
100 TOPS
NeuralChip NC-5000
Production inference and training
Professional
Process
5nm EUV
Cores
64 AI Cores
Memory
128GB HBM3
Bandwidth
4.8 TB/s
Power
350W TDP
Performance
500 TOPS
NeuralChip NC-10000
Large-scale training and data centers
Enterprise
Process
3nm GAA
Cores
256 AI Cores
Memory
512GB HBM3E
Bandwidth
12.8 TB/s
Power
800W TDP
Performance
2000 TOPS
Architecture Features
AI Core Architecture
- Custom tensor processing units (TPUs)
- Mixed-precision FP32/FP16/INT8 support
- Hardware-accelerated sparse operations
- Optimized matrix multiplication units
Memory Subsystem
- High-bandwidth memory (HBM) integration
- Multi-level cache hierarchy (L1/L2/L3)
- Advanced memory prefetching
- On-chip SRAM for ultra-low latency
Interconnect
- Proprietary NeuralLink fabric
- PCIe Gen 5 host interface
- Multi-chip scaling support
- Low-latency chip-to-chip communication
Software Stack
- CUDA-compatible programming model
- PyTorch and TensorFlow native support
- Custom compiler optimizations
- Automatic kernel fusion
Supported Frameworks
| Framework | Version | Support Status |
|---|---|---|
| PyTorch | 2.0+ | Full Support |
| TensorFlow | 2.12+ | Full Support |
| ONNX | 1.14+ | Full Support |
| JAX | 0.4+ | Beta |
| MXNet | 1.9+ | Community |
Chip Design Capabilities
Our hardware is designed to efficiently execute the 70+ chip design algorithms available in our platform:
17
Algorithm Categories
75+
Total Algorithms
10x
Faster than CPU
5x
Energy Efficient
Ready to Get Started?
Contact our sales team to discuss which NeuralChip product is right for your use case.