Cut MoE Inference
Costs by 30-50%
Entropy-guided dynamic expert selection for Mixture-of-Experts models. Same accuracy, dramatically lower compute. Validated on Nemotron 3 Nano, Mixtral, Qwen-MoE, and OLMoE.
def select_experts(router_logits):
# Compute routing entropy
probs = softmax(router_logits)
H = -sum(p * log(p))
# Low entropy = confident routing
# Use fewer experts!
if H < 0.6:
K = 1 # 87.5% compute saved
elif H < 1.2:
K = 2 # 75% compute saved
else:
K = 4 # Full routing
return top_k(probs, K)Validated Results
Real compute savings on production MoE models. Accuracy measured relative to full Top-K routing baseline.
Nemotron 3 Nano
MoENVIDIA Nemotron 3 Nano: 128 experts, validated Jan 2026
Mixtral 8x7B
MoEK=1 used 78% of the time with minimal quality loss
Qwen-MoE
MoEEffective across all entropy thresholds
OLMoE-1B-7B
MoEConsistent savings on smaller MoE architecture
🔬 Multiplicative Savings: Technique Combinations
Adaptive-K stacks with other optimizations. Savings multiply, not just add.
Adaptive-K + Early Exit
COMBOAdaptive-K + ToMe
COMBOTriple Combo
MAX💡 Key Insight: Adaptive-K reduces experts per token, Early Exit skips layers, Token Pruning (ToMe) reduces sequence length. Combined: 0.69 × 0.687 × 0.65 = 0.093 (90.7% savings). See Whitepaper Proposition 7.1.
How It Works
Adaptive-K uses information theory to make intelligent routing decisions. The key insight: routing entropy predicts when fewer experts are sufficient.
Compute Router Entropy
For each token, calculate the entropy H of the router softmax distribution. Low entropy = confident routing.
H = -sum(p * log(p))Dynamic K Selection
Based on entropy thresholds, select fewer experts for confident tokens, more for uncertain ones.
K = 1 if H < 0.6 else (2 if H < 1.2 else 4)Sparse Expert Execution
Only execute the selected K experts. Skip unnecessary computation entirely.
output = sum(expert[i](x) * w[i] for i in top_k)The Key Insight
When the router is confident (low entropy), it has already identified the "right" expert. Running additional experts adds compute cost but minimal value. By dynamically adjusting K based on entropy, we skip unnecessary work while maintaining output quality.
Production Observability
Monitor, debug, and optimize your Adaptive-K deployment with built-in observability tools.
Prometheus Metrics
Production-ready metrics: latency, throughput, K distribution, compute savings
metrics.start_http_server(9090)
# adaptive_k_latency_seconds
# adaptive_k_avg_k
# adaptive_k_compute_saved_ratioStructured Logging
JSON-formatted logs for ELK, Datadog, or any log aggregator
logger = get_logger("inference")
logger.log_inference(trace)
# {"ts":"...", "avg_k":1.5, "latency_ms":45}Tracing & Debug
Per-layer entropy analysis and K selection visualization
debugger.trace_k_selection(entropies)
# Layer 0 | H=0.42 ████░░░░ | K=1
# Layer 1 | H=1.23 ████████ | K=4A/B Testing
Built-in framework to compare Adaptive-K vs Full-K in production
ab_test.assign_variant(request_id)
ab_test.compute_results()
# Latency: -32%, Quality: -0.1%Install with observability support:
pip install adaptive-k-routing[observability]