Definition
LLM Optimization encompasses techniques to improve language model efficiency in terms of speed, memory, latency, and cost. Critical for scaling LLMs beyond research to production deployments.
Key Bottlenecks in Production
- Memory bandwidth: Activations must move through GPU/TPU (expensive)
- Compute/memory ratio: Transformers are memory-bound, not compute-bound
- Attention complexity: O(n²) with sequence length
- KV cache growth: Linear memory with context length
Optimization Techniques
Memory Efficiency
- quantization: 32-bit → 8-bit, 4-bit, 3-bit
- kv-cache compression (6x reduction with turboquant)
- Pruning: Remove unused weights
Compute Efficiency
- Flash Attention: Reduce memory movement during attention
- Pipelining: Parallelize across devices
- Batching: Process multiple requests simultaneously
Model Architecture
- mixture-of-experts: Conditional computation (sparse activation)
- Multi-Query Attention: Share attention heads
- Low-rank approximations: Replace layers with low-rank matrices
Automated Training Optimization (June 2026)
recursive’s automated research system achieved SOTA on training benchmarks:
- nanochat-autoresearch: 0.9109 validation BPB (1.3× speedup vs community best)
- nanogpt-speedrun: 77.5s vs 79.7s on 2+ year community baseline
- Key discovery: hashed bigram/trigram embeddings in short-context memory path
(2026-06-11-recursive-automated-ai-research)
turboquant Breakthrough (April 2026)
google achieved:
- 6x kv-cache compression via 3-bit quantization
- 8x speedup on H100 GPUs
- Zero accuracy loss
- Data-oblivious (works on any model)
Enables:
- Cost reduction: X/6 for serving
- Longer context: Same memory = 6x context length
- Higher throughput: More tokens/second per GPU
Industry Implications
For cloud LLM APIs:
- Reduced cost per token (pricing pressure on OpenAI)
- Competitive advantage for optimized inference
For open-source:
- Local deployment becomes viable (edge deployment)
- gemma-4, mistral easier to run on consumer hardware