Which AI chip should I care about?
The answer depends on the job: frontier training, cheap inference, long context, software portability, supply assurance, or power-limited deployment.
There is no universal best chip. There is a best chip for a constraint set, and the constraint set changes as the bottleneck moves across power, memory, fabric, and software.
Start with the workload
A frontier training run wants scale-up bandwidth, reliable collectives, mature kernels, and enough software flexibility for researchers to change the model. A high-volume inference service wants cost per token, HBM bandwidth, predictable latency, and power efficiency.
Those are different jobs. Treating every accelerator as a generic "GPU equivalent" erases the decision that actually matters.
NVIDIA is the default because the system works
NVIDIA sells more than silicon. It sells CUDA, NVLink, rack designs, reference systems, libraries, debugging tools, and an ecosystem that lets teams move quickly.
That default can be expensive and still rational. When the cost of delay is a missed frontier cycle, software risk is not a footnote.
Custom silicon is how hyperscalers claw back margin
Google TPUs, AWS Trainium, Meta silicon, and other custom programs are attempts to own the cost curve for repeatable internal workloads. They do not need to win every workload. They need to win the workloads their owners serve at massive scale.
The more inference stabilizes, the more custom chips matter. The more model architecture changes, the more flexible accelerators keep their premium.
The comparator is a constraint map
Read the rows as trade-offs. HBM bandwidth points at inference. Scale-up domain points at training shape. TDP points at site power and cooling. Release date points at supply risk and software maturity.
The best buyer asks which line item binds their roadmap first, then works backward from that line item to the chip.
| Chip | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| TPU 8I (Inference) | 2026-Q1 | 5.5 | 10.5 | 256 | 9.0 | 12,288 | 750 | ||
| TPU v7p (Ironwood) | 2025-Q4 | 4.6 | — | 192 | 7.4 | 9,216 | 720 | ||
| MI355X | AMD | 2025-Q4 | 5.0 | 10.1 | 288 | 8.0 | 8 | 1,400 | |
| B200 | NVIDIA | 2024-Q4 | 4.5 | 9.0 | 192 | 8.0 | 72 | 1,000 | |
| GB200 (Grace+B200) | NVIDIA | 2024-Q4 | 9.0 | 18.0 | 384 | 16.0 | 72 | 2,700 | |
| TPU v6 (Trillium) | 2024-Q4 | 0.92 | — | 32 | 1.6 | 256 | 350 | ||
| MI325X | AMD | 2024-Q4 | 2.6 | — | 256 | 6.0 | 8 | 1,000 | |
| H200 | NVIDIA | 2024-Q1 | 2.0 | — | 141 | 4.8 | 8 | 700 | |
| H100 | NVIDIA | 2022-Q3 | 2.0 | — | 80 | 3.4 | 8 | 700 |