LLM INFERENCE OPTIMIZATION
Revolutionizing LLM Serving with Hardware-Efficient W4A8 GEMM
LiquidGEMM introduces a breakthrough in W4A8 quantization for Large Language Models, overcoming existing bottlenecks with innovative hardware-aware designs to deliver unprecedented performance and efficiency for production deployments.
Unlocking Unprecedented Performance for LLMs
LiquidGEMM's innovations in W4A8 quantization and GEMM kernel optimization lead to significant performance leaps, dramatically reducing inference latency and increasing throughput for demanding LLM workloads.
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End-to-End System Speedup
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GEMM Kernel Speedup
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TensorRT-LLM Performance Gain
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Arithmetic Instructions for Dequantization
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The W4A8 Dequantization Bottleneck
Existing W4A8 GEMM kernels are severely limited by inefficient dequantization on CUDA Cores, which cannot keep pace with the high throughput of Tensor Cores. This results in performance significantly below theoretical potential, especially in compute-bound scenarios.
Enterprise Process Flow: W4A8 GEMM Execution Pipeline
Load Weights from GMEM
→
Dequantize on CUDA Cores
→
Execute MMA on Tensor Cores
→
Write Results to GMEM
21%
of Warp Stalls due to Dequantization
LiquidQuant: Hardware-Efficient Dequantization
LiquidQuant addresses overflow issues and high computational burden by employing a rotation-based transformation and a sweet dequantization strategy. It recovers original INT8 values within UINT8 range without overflow, using just two 32-bit hardware instructions (IMAD and XOR) per four elements.
Real-World Deployment: LiquidGEMM in LLM Serving
LiquidGEMM is currently deployed as the primary GEMM kernel in our production LLM serving infrastructure. This strategic integration has demonstrably improved LLM inference efficiency across various models, achieving critical speedups in real-world scenarios. Our hardware-aware design and innovative quantization approach have translated directly into tangible performance benefits, proving its robustness and scalability for high-performance LLM inference.
2
Arithmetic Instructions for Dequantization (per 4 elements)
Implicit Fine-Grained Pipeline (ImFP)
ImFP overcomes the overheads of explicit pipelines by adopting a single-producer, multiple-consumer model. It assigns a unified Compute WG for both dequantization and MMA, eliminating round-trip data movement and achieving implicit parallelism without software synchronization, fully overlapping stages.
Enterprise Process Flow: W4A8 GEMM Execution Pipeline
Load Weights from GMEM
→
Dequantize on CUDA Cores
→
Execute MMA on Tensor Cores
→
Write Results to GMEM
| Feature | LiquidGEMM (W4A8) | TensorRT-LLM (W8A8/W4A16) | QServe (W4A8) |
|---|---|---|---|
| Quantization Type | W4A8 | W8A8, W4A16, FP8 | W4A8 |
| Dequantization Efficiency | Hardware-efficient (2 instructions/4 elements), overflow-safe | Mixed-precision, epilogue dequantization (W8A8) or less efficient CUDA core (W4A16) | CUDA Core bottleneck (dozens of instructions), overflow issues |
| Pipeline Architecture | Implicit Fine-Grained Pipeline (ImFP), full overlap | Explicit pipelines, potential serialization | Explicit coarse-grained pipeline, sync overhead |
| Key Benefits |
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| Performance Gains |
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Unmatched Performance & Efficiency
LiquidGEMM delivers up to 2.90x kernel speedup and 4.94x end-to-end system-level speedup over state-of-the-art W4A8 kernels. It also shows 1.12-1.63x performance gains compared to various quantized GEMM kernels in NVIDIA TensorRT-LLM, ensuring superior LLM serving.
| Feature | LiquidGEMM (W4A8) | TensorRT-LLM (W8A8/W4A16) | QServe (W4A8) |
|---|---|---|---|
| Quantization Type | W4A8 | W8A8, W4A16, FP8 | W4A8 |
| Dequantization Efficiency | Hardware-efficient (2 instructions/4 elements), overflow-safe | Mixed-precision, epilogue dequantization (W8A8) or less efficient CUDA core (W4A16) | CUDA Core bottleneck (dozens of instructions), overflow issues |
| Pipeline Architecture | Implicit Fine-Grained Pipeline (ImFP), full overlap | Explicit pipelines, potential serialization | Explicit coarse-grained pipeline, sync overhead |
| Key Benefits |
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| Performance Gains |
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4.94x
End-to-End Speedup over SOTA W4A8
Quantify Your LLM Inference Efficiency Gains
Use our calculator to estimate the potential time and cost savings by adopting LiquidGEMM's optimized W4A8 quantization for your enterprise LLM workloads.
Your Path to Accelerated LLM Performance
A phased approach to integrate LiquidGEMM into your existing LLM serving infrastructure, ensuring a smooth transition and maximum impact.
Phase 1: Performance Assessment & Strategy
Evaluate current LLM serving performance, identify bottlenecks, and define a tailored integration strategy for LiquidGEMM's W4A8 kernel.
Phase 2: LiquidGEMM Integration & Testing
Implement LiquidGEMM with LiquidQuant and ImFP into your inference stack. Conduct rigorous testing and benchmarking against existing baselines.
Phase 3: Optimization & Production Deployment
Fine-tune parameters for optimal performance. Deploy LiquidGEMM in your production environment, monitoring for real-world efficiency gains.
Phase 4: Continuous Improvement & Scaling
Leverage ongoing support and updates to scale your LLM serving capabilities and maintain peak hardware efficiency.
Ready to Transform Your LLM Infrastructure?
Partner with us to integrate LiquidGEMM and unlock the full potential of hardware-efficient W4A8 quantization for your Large Language Models.
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Book Your Free Consultation.
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