HyperSparse

Now in Early Access
0x

Cut LLM Inference Costs 60–80%.

Drop-in gateway. Works on any frozen model. Quality guaranteed.

Request Early AccessSee Benchmarks
7 Architectures Validated100% Token Agreement

The Problem

GPU Compute Is the Biggest Cost in AI.

$30B+
Annual LLM Inference Market
Growing 40%+ YoY
#1
GPU Cost Is the Top Complaint
From every AI company
70%
Of AI Spend Is Inference
Not training

Every token through every layer. Even when the model already knows the answer. We eliminate that waste — adaptively, per token, per layer — with zero quality loss.

Benchmarks

Production Results. Not Projections.

All benchmarks on NVIDIA A100 80GB with vLLM. Wall-clock speedup, not theoretical FLOP savings. Output verified identical to dense baseline.

Llama 3.1 70B AWQ-INT4 — DCC v4 — MLP + Sparse-Q + Lean1.97x
Dense: 51.3 tok/s
HyperSparse: 101.1 tok/s
Llama 3.1 70B AWQ-INT4 — E2E Throughput (8K in, 128 out)1.74x
Dense: 16.9 tok/s
HyperSparse: 29.3 tok/s
Mistral 7B — DCC v4 — Conservative1.09x
Dense: 213 tok/s
HyperSparse: 232 tok/s
0x
TTFT Speedup
Llama 70B, 8K context
0%
Token Agreement
Zero output degradation
0%
Compute Saved
Per prefill request
0
Architectures
7B to 72B validated

Speedup scales with model size. 1.09x at 7B → 1.97x at 70B. The bigger the model, the more compute-bound it is, the more you save.

Integration

One Line of Code.
60–80% Cheaper.

HyperSparse is an OpenAI-compatible gateway. Point your client at our URL and every request is automatically routed, cached, and compressed. No model changes. No retraining.

Works on any frozen, off-the-shelf model
OpenAI-compatible — swap one URL
1.97x prefill speedup at 100% agreement
Quality certificates in every response
integrate.py
from openai import OpenAI
 
# Before
client = OpenAI()
 
# After — that's it
client = OpenAI(base_url="https://gateway.hypersparseai.com/v1")
 
# 60-80% cheaper. Identical outputs.

How It Works

Three Layers. One Gateway.
Compounding Savings.

Each optimization covers a different part of inference. Together they compound to 60–80% cost reduction.

Pre-inference

Smart Routing

Automatically sends each request to the cheapest model that handles it well. Simple queries go to 7B. Complex ones go to 70B.

40–60% savings
Cache layer

Semantic Caching

Recognizes semantically similar queries and returns instant responses. No model call, no cost, no latency.

10–30% savings
Compute

DCC

Dynamic Compute Compression scores token importance and skips 67% of MLP computation — per token, per layer — with zero quality loss.

1.97x TTFT speedup
✓

Quality certificates in every API response

Every response includes a cert_Q score — mathematical proof that the output matches dense quality. If quality dips, automatic dense fallback kicks in. Zero risk.

Built For

From API Providers to Enterprises.

Anyone paying for GPU inference benefits. The bigger the spend, the bigger the savings.

Tier 1

AI API Providers

Together AI, Fireworks AI, Anyscale, Replicate

Margins are directly tied to GPU efficiency. 1.97x prefill speedup lets them slash prices or pocket the margin.

$100K–500K+ ARRper deal
Tier 2

Self-Hosted Enterprises

Fintech, healthcare, legal tech, AI startups

Spending $50K–500K+/mo on inference. One URL change — instant 60-80% cost reduction, zero code changes.

$50K–200K ARRper deal
Tier 3

GPU Cloud Platforms

Lambda Labs, CoreWeave, RunPod

Bundle HyperSparse as a value-add. Increase customer stickiness and differentiate their platform.

Partnershipper deal

Ready to Cut Inference Costs 60–80%?

Join the waitlist. Be first to deploy production-grade LLM optimization with mathematically guaranteed quality.

No spam. We'll reach out when early access is ready.