Next-Gen AI Inference: ASIC vs GPU Performance Analysis

The Silicon Decision: Why Architecture Matters

When building AI infrastructure at scale, the choice between GPUs and custom ASICs fundamentally shapes your cost structure, power consumption, and performance ceiling. This analysis breaks down the real-world trade-offs.

GPU: The General-Purpose Workhorse

GPUs have dominated AI workloads since the deep learning revolution. Their strengths are well-documented:

• Flexibility: Same hardware runs training, inference, and fine-tuning
• Ecosystem: Mature tooling with CUDA, cuDNN, and extensive library support
• Availability: Multiple vendors, established supply chains

However, GPUs carry inherent inefficiencies for inference-only workloads:

• Over-provisioned memory bandwidth for most inference tasks
• Power draw optimised for training, not serving
• Thermal design assumes sustained high-utilisation bursts

ASIC: Purpose-Built for Inference

Application-Specific Integrated Circuits strip away general-purpose overhead. When designed specifically for transformer inference, ASICs deliver:

Power Efficiency

Our benchmarks show 70% lower power consumption per token compared to equivalent GPU deployments. This translates directly to:

• Lower electricity costs
• Reduced cooling requirements
• Higher rack density

Throughput

Custom silicon achieves 5× performance per watt by:

• Eliminating unused execution units
• Optimising memory hierarchy for attention patterns
• Reducing data movement between compute and memory

Latency Consistency

ASICs deliver predictable sub-100ms latency with minimal variance—critical for production SLAs.

When to Choose Each

Choose GPUs when:

• You need training and inference on the same hardware
• Workloads change frequently
• You're prototyping or experimenting

Choose ASICs when:

• Inference is your primary workload
• Cost-per-token matters at scale
• Power constraints limit expansion
• You need consistent latency guarantees

The Hybrid Approach

Many production deployments benefit from a hybrid architecture:

• GPUs for fine-tuning, experimentation, and low-volume inference
• ASICs for high-volume, latency-sensitive production inference

This approach maximises flexibility while optimising for cost where it matters most.

Conclusion

The "GPU vs ASIC" question isn't about which is universally better—it's about matching silicon to workload. For high-volume inference at scale, custom silicon delivers compelling economics that improve with every token served.

For more information on our infrastructure approach, contact info@scx.ai.