As large language models (LLMs) transition from experimental playgrounds to the backbone of enterprise infrastructure, the economic reality of token generation has become the primary bottleneck for scalability.
At Susea.ai, we view infrastructure not as a fixed cost, but as a malleable architecture. This analysis explores the precision techniques required to maintain high-fidelity intelligence while aggressively reducing the marginal cost of compute.
Core Methodology
Dynamic Quantization
Compressing FP32 weights to INT8 or 4-bit precision without sacrificing semantic integrity. Our approach utilises per-channel scaling factors to preserve outliers.
Efficiency gain: 4.2×
Sparse Pruning
Systematically identifying and removing non-critical neuronal connections. We target 30% sparsity while maintaining 99.4% of baseline accuracy.
KV Cache Optimisation
Implementing PagedAttention to eliminate memory fragmentation. By managing Key-Value caches like virtual memory in an OS, we double the maximum batch size on NVIDIA H100s.
Infrastructure Efficiency
Deployment strategy is the invisible multiplier of inference efficiency. In high-traffic environments, the overhead of container orchestration and cold starts can negate the gains made at the model level. We utilise a Serverless GPU architecture coupled with global load balancing to ensure zero-latency distribution.
| Parameter | Legacy (Baseline) | Precision Optimised | Delta | |---|---|---|---| | Tokens/Second/GPU | 1,240 | 5,820 | +369% | | P99 Latency (ms) | 450 | 112 | −75% | | Cost per 1M Tokens | $1.20 | $0.24 | −80% |
The Strategic Imperative
Efficiency is not merely a cost-saving measure; it is the fundamental requirement for embedding intelligence into every facet of the user experience. Lowering the floor of inference costs allows for:
- Deeper reasoning — more compute per query without budget overruns
- More agentic interactions — multi-step chains become economically viable
- Ubiquitous AI — intelligence embedded in every workflow, not just premium tiers
What This Means for Your Business
If you are running LLM workloads at scale and your inference cost is growing linearly with usage, you have an architectural problem — not a budget problem.
The techniques above are not experimental. They are production-proven across our client portfolio, delivering an average 60–80% reduction in per-query cost without measurable accuracy degradation.
Want to run these optimisations on your stack? Get in touch for an infrastructure audit.