01 / INFERENCE ACCELERATION — 4 AGENTS

Your model is slow.
We make it argue with physics.

Drop in a bottleneck — “Llama 70B at 4.2s/token on A100”. WARP spins up 4 hostile optimizers that fight over quantization, kernels, and KV-cache until you get a shippable plan.

● 7.4× AVG SPEEDUP● 63% COST CUT● NO ACCURACY LOSS
LIVE BOTTLENECK — AUTO-PROFILING 01 / 05
Click to pause • ENTER to optimize
QUANTIZE • FUSE • CACHE • FLASH-ATTN-3 • PAGED KV • TENSORRT • AWQ • GPTQ • DISTILL • SPECULATE • QUANTIZE • FUSE • CACHE • FLASH-ATTN-3 • PAGED KV • TENSORRT • AWQ • GPTQ • DISTILL • SPECULATE •
02 / LIVE OPTIMIZER COUNCIL

Four optimizers.
One bottleneck.
No mercy.

Same color logic, new meaning: RED = Bottleneck, ORANGE = Quant, PURPLE = Kernels, LIME = Cost/ROI. They debate like a PR review, you get the merged plan.
03 / HOW IT WORKS

Profile → Fight → Fuse → Ship faster.

01

Drop
Bottleneck

“SDXL 12s/image on A10G”. We parse model, hardware, batch, SLO.

02

Auto
Profile

Simulates roofline, memory vs compute bound, KV pressure, kernel breakdown.

03

Hostile
Debate

Quant vs Kernel vs Systems vs Cost argue. Best trade wins, not loudest.

04

Shippable
Plan

Diff, Dockerfile, Triton kernels, and cost math. Copy-paste to prod.

04 / BENCHMARK REPORT

WARP REPORT
Llama 70B — A100

Latency p95
4.2s → 0.61s
7× faster. Same quality. FlashAttention-3 + paged KV + AWQ-4bit.
Throughput
1.2 → 8.4 tok/s
Cost / 1M tok
$14.2 → $2.1
05 / OPTIMIZATION PLAN
MERGED INSIGHT

Don't quantize everything. Quantize the bottleneck layers, fuse the rest, and let speculation cover tail latency.

06 / SHIP

Stop paying
for slowness.

Free profile. No account. Export as Dockerfile + benchmarks.