More tokens.Same GPUs.
Software acceleration that scales tokens/sec, not your hardware budget.
lopolith run --model llama-8b --dtype bf16 --seq 8192 --iters 50 --warmup 10
WORKS WITH
PyTorch · vLLM · TensorRT‑LLM · CUDA
Optimize Every Compute Cycle.
Maximize Every Dollar.
185.2
tokens/sec
Optimized · 5.4ms median latency
69.6
tokens/sec
Baseline · 14.4ms median latency
2.66×
TPS Gain (Median)
Measured via CUDA Events
Verified Performance
Every optimization comes with checksummed JSON certificates. TPS measured directly from median latency using CUDA Event timing with synchronize.
Certificates with SHA256 checksum
Reproducible with exact commands
Git SHA & timestamp tracking
Intelligent Optimization
Automatic backend selection with correctness-first fallback. Persistent kernels with fused-Q path on SM≥120. Per-operation profiling with roofline analysis.
Parity checks with auto-demotion
Small-batch pipelining (B=2)
Per-kernel performance metrics
Production Ready
Deploy with confidence using safe fallback mechanisms, preallocated IO buffers, and comprehensive observability.
Safe fallback to baseline ops
Preallocated IO buffers
Optional GPU sampling
Chrome trace exports
Native Framework Integration
Direct integration with PyTorch modules, vLLM plugin support, and TensorRT-LLM compatibility. Enable with a single environment variable.
PyTorch Native
GemvSupreme, DecodeAttention
vLLM Plugin
LOPOLITH_VLLM_PLUGIN=1
TensorRT-LLM
LOPOLITH_TRTLLM_PLUGIN=1
CUDA Extensions
TRITON, CUDA_EXT backends