In 2026, the AI chip market has entered a new phase of “inference-first, training-secondary.” As large language models advance from hundreds of billions to trillions of parameters, training costs continue to climb, yet the large-scale deployment demand for inference is even more urgent. According to IDC, the AI inference chip market will exceed the training chip market for the first time in 2026, reaching a 55% share.
On the training side, NVIDIA’s Rubin platform costs $100,000-120,000 per unit, and a single GPT-5-level training run is estimated to exceed $100 million. On the inference side, although per-chip cost is lower, the deployment scale is enormous – globally, more than 2 million AI inference servers are expected, and inference chip demand is over 5x that of training chips.
2026 Inference Chip Market Share
Global AI Inference Servers
Inference / Training Chip Demand Ratio
Training Costs: Triple Pressure of Compute, Memory, and Power
In the cost structure of large-model training, compute hardware accounts for 40%, high-bandwidth memory (HBM) for 25%, and power and cooling for 20%, with the remainder being network and software. HBM3e prices keep rising; 12-layer 8-Hi stacked modules cost over $800 each, becoming the core cost item in training servers second only to the GPU.
Power cost is another hidden pressure. A single 8-GPU Rubin server consumes over 10kW, and the power demand of large training clusters is comparable to that of a medium-sized city. Liquid cooling has shifted from optional to standard; direct liquid cooling (DLC) costs 30% more than air cooling, but reduces PUE from 1.4 to 1.05.
Inference Efficiency: Quantization and Pruning Become Imperative
The core technical paths to optimize inference cost are quantization and pruning. INT8 quantization can reduce inference power by 75% while keeping accuracy loss within 1%; INT4 quantization reduces power further but requires a mixed-precision strategy to preserve the accuracy of critical layers.
At the chip level, inference accelerators are diverging into two routes: one is general-purpose GPU inference (NVIDIA L40S, AMD MI300X), supporting flexible multi-model deployment; the other is ASIC-specific inference (Google TPU v5, Huawei Ascend 910B), with single-model inference energy efficiency 3-5x higher than GPUs.
| Metric | GPU General Inference | ASIC Dedicated Inference |
|---|---|---|
| Energy Efficiency | Baseline 1x | 3-5x |
| Flexibility | Multi-model support | Single/limited models |
| Deployment Cycle | Fast, mature software | Longer, needs custom dev |
| Power | 300-400W/card | 75-150W/card |
| TCO (3yr) | Higher | 30-50% lower |
Progress of Domestic AI Chips
Domestic AI inference chips achieved multiple breakthroughs in 2026. Cambricon’s Siyuan 370 chip delivers 256 TOPS of INT8 inference compute and supports mainstream large-model deployment. Enflame’s T20 inference card offers better cost-performance than international competitors in vision and speech scenarios. MetaX’s C500 adopts a self-developed MAC architecture with 160 TFLOPS of FP16 compute, targeting the data center inference market.
Although domestic AI chips still lag behind NVIDIA in absolute compute, their cost-performance in specific inference scenarios (such as visual inspection, speech recognition, and natural language understanding) is catching up. Together with the advancement of domestic HBM alternatives (CXMT’s CX-HBM1), the full-stack domestic AI inference solution is expected to achieve large-scale deployment in 2027.
Analysis of AI chip inference and training costs shows that the scaling and efficiency optimization of the inference side is the most urgent industry need today. Three parallel paths – quantization technology, dedicated ASICs, and domestic substitution – will jointly drive AI inference costs down. Honchak Electronics will continue to provide the latest developments and selection support for the AI chip supply chain.