⚡ Inference Systems· 8 min read
Custom Silicon
Groq LPU, Cerebras WSE, AWS Trainium/Inferentia, Google TPU — inference-specific chip tradeoffs and when they make sense.
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NVIDIA GPUs dominate AI inference, but alternative chips optimize for specific workloads — often achieving better cost-efficiency for the right use case.
1. The Alternatives
| Chip | Maker | Key innovation | Best for |
|---|---|---|---|
| H100/B200 | NVIDIA | General-purpose, ecosystem | Everything (default choice) |
| Groq LPU | Groq | SRAM-only (no HBM), deterministic latency | Single-user low-latency decode |
| Cerebras WSE | Cerebras | Wafer-scale (entire wafer = 1 chip) | Very large models, research |
| Inferentia2 | AWS | Cost-optimized for inference | AWS-native, cost-sensitive |
| Trainium2 | AWS | Training + inference | AWS ecosystem |
| TPU v5e | Cost-optimized, MXU matrix units | Google Cloud, large batch | |
| Gaudi 3 | Intel | High HBM bandwidth, Ethernet native | Alternative to H100, cost play |
| Ascend 910B | Huawei | Domestic Chinese market | China market (export controls) |
2. When Alternatives Win
| Scenario | Best choice | Why |
|---|---|---|
| General purpose, maximum flexibility | NVIDIA H100/B200 | Ecosystem, software support, versatility |
| Lowest latency per token (single user) | Groq LPU | SRAM eliminates HBM bottleneck |
| AWS-native, cost-sensitive batch | Inferentia2 | 2-3× cheaper per token than H100 on AWS |
| Google Cloud, large-scale serving | TPU v5e | Integrated with GCP, good cost/perf |
| China market (export restricted) | Ascend 910B | Only available option |
The NVIDIA Lock-In Question
NVIDIA’s advantage isn’t just hardware — it’s the software ecosystem (CUDA, TensorRT, vLLM, NCCL). Switching to alternative silicon means:
- Rewriting or porting inference engines
- Limited framework support (PyTorch/JAX may not fully optimize)
- Smaller community, fewer pre-built solutions
- Risk of vendor-specific bugs and limitations
The calculus: Alternative silicon needs to be 3-5× cheaper (not just 1.5×) to justify the switching cost and ecosystem risk.
3. Evaluation Framework
| Dimension | What to measure | Why |
|---|---|---|
| Cost per token | $/M tokens at target SLA | The bottom line |
| Latency | TTFT and TPOT at production load | User experience |
| Model support | Does your specific model work? | Not all models run on all chips |
| Quantization support | INT4, FP8 on this hardware? | Key optimization may not be available |
| Ecosystem maturity | Debugging tools, community, documentation | Operational cost |
| Scaling path | Multi-chip, multi-node? | Future growth |
For the cost analysis framework, see The Inference Cost Stack. For multi-model approaches that reduce cost without custom hardware, see Multi-Model Serving.
Self-Check
- Compare NVIDIA H100 vs 3 alternative chips on cost/latency/ecosystem
- Explain the NVIDIA ecosystem advantage and switching cost
- Determine when alternative silicon provides enough cost savings to justify switching
- Evaluate a chip using the 6-dimension framework