ASIC (Application-Specific Integrated Circuit) Architecture
What is an ASIC
ASIC (Application-Specific Integrated Circuit) is a custom AI accelerator designed for specific applications. Compared to the general-purpose nature of GPUs, ASICs deliver higher energy efficiency and lower per-token cost for specific workloads.
Representative products:
- AWS Trainium / Trainium 2: Training + inference fungible
- AWS Inferentia / Inferentia 2: Inference only
- Qualcomm AI 100 (AIC100): Low-power data center inference
- Google TPU (partially classified as ASIC)
ASIC vs GPU
| Dimension | ASIC | GPU |
|---|---|---|
| General-purpose | Weak (specific workloads) | Strong (any AI task) |
| Energy efficiency | 2-3× better than GPU | Medium |
| Performance/watt | High | Medium |
| Per-token cost | Low | Medium |
| Development cycle | 2-3 years | 1-2 years |
| Ecosystem | Vendor-proprietary | CUDA mature |
| Flexible upgrades | Difficult (fixed tape-out) | Easy (driver updates) |
| Best for | Large-scale inference | General AI |
AWS Trainium / Inferentia
AWS Trainium
- Trainium 1 (2020): First AWS in-house training chip
- Trainium 2 (2024-12 GA): 96GB HBM, 1,299 FP8 TFLOPS, 4× Trainium 1
- Trainium 3 (late 2025): Rumored 2× Trainium 2
- NeuronLink interconnect, 64-chip UltraServer
- Neuron SDK (PyTorch / TensorFlow integration)
- Customers: Anthropic, AWS internal
AWS Inferentia
- Inferentia 1 (2019): 128 TOPS INT8
- Inferentia 2 (2023): 32GB HBM2e, ~190 TOPS, 12-chip interconnect
- Inf1 / Inf2 instances (AWS EC2 rental)
- Inference cost 70% lower than GPU
Qualcomm AI 100 (AIC100)
- Released 2020 (pre-pandemic)
- 400 TOPS INT8, 75W TDP
- 2.67 TOPS/W (performance/watt leads GPU)
- Qualcomm AI Engine Direct SDK
- Customers: Hugging Face Inference API, Oracle Cloud
ASIC Use Cases
- ✅ Large-scale data center inference (Inf2, Trn2)
- ✅ Ultra-large LLM inference (Hugging Face)
- ✅ Training + inference fungible (Trainium 2)
- ✅ Cost-effective inference (Inferentia 1/2)
- ✅ Low-power data center (Qualcomm AI 100)
- ❌ Multi-task general purpose (use GPU)
- ❌ Rapid new algorithm iteration (use GPU)
Detailed Product Pages
AWS
- AWS Trainium - 2019 first-gen training, Neuron SDK
- AWS Trainium 2 - 2024-12 GA, 96GB HBM 1299 FP8 TFLOPS 4× Trainium 1
- AWS Trainium 3 - 2025-12-02 GA, 3nm 144GB HBM 2.7 TB/s 4.4× Trainium 2, NeuronLink-V3
- AWS Inferentia - 2019 first-gen inference, 128 TOPS INT8
- AWS Inferentia 2 - 2023 second-gen inference, 32GB HBM2e 190 TOPS
Qualcomm
- Qualcomm Cloud AI 100 (AIC100) - 2020, 400 TOPS INT8 75W
- Qualcomm AI 200 / AI 300 - 2025-2026, 5nm Hexagon NPU + Oryon CPU Cloud inference
Chinese AI Startups
- Cambricon MLU 370 - 2021-Q4 7nm 96 INT8 TOPS 35W (EOL 2023)
- Cambricon MLU 590 - 7nm 96GB HBM2 256 INT8 TOPS 250W, STAR Market
- Cambricon MLU 690 - 2025-2026 estimated 5nm 192GB HBM3E 2 PF FP8
- Biren BR104 - 7nm 1024 INT8 TOPS 64GB HBM2E dual chiplet 300W $700M+ funding
- Moore Threads MTT S5000 - 7nm 48GB GDDR6 50 BF16 TF MUSA architecture
- Alibaba Hanguang 800 - 12nm 820 INT8 TOPS 168W 100K+ deployed
Tenstorrent
- Tenstorrent Blackhole - 120 Tensix cores 5 RISC-V/core 8GB SRAM 16 BF16 PF cluster Jim Keller architecture
Related Architectures
- GPU - General AI
- TPU - Google data center
- LPU - Ultra-low latency LLM
- NPU - Edge/data center NPU
- Complete Comparison Table