AI/ML Performance Optimization
RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts
RaMP introduces a novel routing-aware dispatch framework for Mixture-of-Experts (MoE) models, addressing the inefficiency of static kernel configurations. By analyzing real-time expert routing distributions and utilizing a physically grounded four-parameter wave cost model, RaMP achieves significant performance improvements. It delivers up to 1.30x end-to-end speedup in vLLM serving compared to existing solutions, making MoE inference more efficient and cost-effective across various architectures.
Unlocking Peak MoE Efficiency
RaMP delivers tangible performance gains by intelligently adapting to dynamic routing distributions in MoE models, overcoming limitations of static dispatch.
Mean Regret vs. Exhaustive Search
Kernel Speedup over Static Dispatch
End-to-End Speedup in vLLM Serving
One-time Profiling per Model
Deep Analysis & Enterprise Applications
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10-70%
Kernel Throughput Unrealized by Static Dispatch
1.22x
Geomean Performance Left on the Table
| System | Approach | Limitations |
|---|---|---|
| vLLM Triton | Batch-size only |
|
| Alpha-MoE | Batch-size only |
|
| DeepGEMM | Fixed bm=128 |
|
| FlashInfer | Integer configs |
|
| RaMP | Routing-aware dispatch, cost model |
|
The Core Problem: Dynamic Routing
Mixture-of-Experts (MoE) models activate a fraction of parameters per token, meaning the optimal kernel configuration is highly dependent on the runtime expert routing distribution, which changes at every forward step. Existing production systems only dispatch based on batch size, ignoring routing entirely. This leads to 10-70% of kernel throughput being unrealized and a 1.22x geomean performance opportunity left on the table.
Enterprise Process Flow
Offline Config Enumeration & Profiling
→
OLS Wave Cost Model Fit
→
Online Expert Bincount
→
Cost Evaluation & Argmin
→
CuTe DSL Kernel Dispatch
Physically Grounded Cost Model
RaMP's key innovation is a four-parameter wave cost model that encodes startup overhead, wave scheduling, per-CTA memory traffic, and sub-wave nonlinearity. This model depends only on CTA grid geometry, making it kernel-agnostic and allowing dispatch from the actual expert histogram at runtime. It's fitted from just 10-24 minutes of one-time profiling per model.
| Variable | Description | Optimization Impact |
|---|---|---|
| Compute Density (ρ) | WGMMA tile ops per CTA |
|
| L2 Pressure (λ, κ) | Weight footprint vs. L2 capacity |
|
| Wave Utilization (w) | Grid parallelism vs. SM_COUNT |
|
| K-reduction Depth (κ) | Reduction work across CTAs |
|
1.14x
Speedup on Third-Party Alpha-MoE Kernel
1.30x
End-to-End Speedup in vLLM Serving
Real-World Performance Gains: OLMOE-1B-7B-FP8
Deployed in vLLM, RaMP achieves significant speedups across various workloads on OLMOE-1B-7B-FP8:
- 1.30x TPOT geomean over Triton FP8
- 1.41x over DeepGEMM
- 1.13x over FlashInfer CUTLASS
Kernel-Agnostic & Generalizable
The cost model formulation depends only on CTA grid geometry, not the specific kernel implementation. This makes RaMP kernel-agnostic, evidenced by its 1.14x speedup on Alpha-MoE's C++ kernel without source modification. It also correctly predicts optimization applicability for all 8 tested architectures, including 3 unseen models profiled from scratch, confirming its broad applicability.
Calculate Your Potential ROI with RaMP
Estimate the annual efficiency gains and cost savings your enterprise could achieve by implementing RaMP for MoE inference.
Your RaMP Implementation Roadmap
A structured approach to integrating RaMP for maximum impact and minimal disruption.
Phase 1: Discovery & Strategy
Initial consultation to understand your current MoE inference setup, identify bottlenecks, and define performance goals. Develop a tailored strategy for RaMP integration.
Phase 2: Integration & Profiling
Seamless integration of RaMP's dispatch framework into your existing vLLM or custom serving stack. One-time profiling (10-24 minutes per model) to calibrate the cost model.
Phase 3: Optimization & Validation
Deploy RaMP's routing-aware dispatch, monitoring real-time performance. Validate speedups and efficiency gains against predefined KPIs.
Phase 4: Continuous Improvement
Ongoing support and updates to ensure sustained performance as MoE models evolve and hardware changes. Adapt RaMP for new model architectures as needed.
Unlock Peak MoE Performance
Ready to eliminate kernel overhead and achieve significant speedups for your Mixture-of-Experts models? Schedule a free consultation to see how RaMP can transform your inference infrastructure.
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