Optimizing Small LLMs: Why the Standard Playbook Fails Below 1.5B Params
oli266 · reddit · 2026-08-08
Mainstream inference optimizations (like quantization and KV cache compression) are designed for memory-bound large models, but they often do nothing for small models (5-20M params). The author identifies a critical crossover point at around 1.5B parameters.
- Bottleneck Shift: Below 1.5B, inference is launch-bound rather than memory-bound. Streaming bf16 weights takes microseconds, making quantization (like fp8) completely useless.
- What Actually Works: For small models, effective optimizations include batching (combining requests to utilize GPU parallelism), casting weights once at load (avoiding redundant dtype copies), and CUDA Graphs (reducing kernel calls per forward pass from 76 to just 3.4).
- Results: These targeted optimizations yielded a 2.4x throughput increase with zero accuracy loss.
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