Fast LLM Inference: Two Approaches Compared
LLM inference speed matters. Whether you are building AI agents, coding assistants, or real-time chatbots, latency directly impacts user experience and operational costs. Two dominant strategies have emerged: batch size optimization and specialized hardware.
The Batch Size Approach
Anthropic's fast mode delivers 2.5x faster token generation (around 170 tokens per second) by reducing batch sizes. The tradeoff is straightforward: GPUs spend significant time waiting for batches to fill before processing. By guaranteeing immediate processing for premium users, latency drops dramatically.
Think of it like a bus system. Normal inference waits for the bus to fill up. Fast mode means the bus leaves as soon as you board. The cost is roughly 6x higher because you are effectively paying for empty seats.
# Conceptual batch size impact
# High batch = high throughput, high latency
# Low batch = lower throughput, lower latency per request
This approach requires no model changes. You get the exact same model with the same capabilities, just faster.
The Specialized Hardware Approach
OpenAI took a different path by partnering with Cerebras. Their fast mode achieves over 1000 tokens per second, which is 15x faster than standard inference. The secret is Cerebras's massive wafer-scale chips.
A typical H100 GPU is about one square inch. A Cerebras chip is 70 square inches. This allows 44GB of on-chip SRAM, enough to fit smaller models entirely in fast memory without constant weight streaming.
The catch: you need a smaller model. OpenAI's Spark model is a distilled version of GPT-5.3-Codex, optimized to fit on Cerebras hardware. It is faster but less capable, particularly when handling complex tool calls.
Key Differences for SRE Teams
| Factor | Batch Optimization | Specialized Hardware |
|---|---|---|
| Speed gain | 2.5x | 15x |
| Model quality | Identical | Reduced (distilled) |
| Cost multiplier | ~6x | Varies by provider |
| Implementation | Infrastructure change | New hardware stack |
When to Use Each Approach
Choose batch optimization when:
- Model accuracy is critical
- You need the full capability of frontier models
- Your infrastructure can handle the cost premium
Choose specialized hardware when:
- Raw speed matters more than edge-case accuracy
- You are building agentic systems with many rapid tool calls
- You can tolerate occasional model confusion
Operational Considerations
For teams deploying ML inference at scale, the batch size approach is easier to adopt. It works with existing GPU infrastructure and requires no model retraining.
Cerebras and similar wafer-scale chips require specialized deployment pipelines. Model quantization and distillation add complexity, but the speed gains can justify the effort for latency-sensitive applications.
Conclusion
Fast LLM inference is not a one-size-fits-all problem. Batch optimization preserves model quality at higher cost. Specialized hardware delivers raw speed with capability tradeoffs. Understanding both approaches helps you make informed decisions when architecting your ML infrastructure.
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