Competitive Landscape

Hardware vs. Software Inference.

Every competitor builds custom silicon to speed up AI inference. Sibacus eliminates the multiply instead — running on the CPUs you already own.

The Hardware Approach

Build faster multipliers. Spend $100M–$500M on custom ASICs. Lock customers into proprietary ecosystems.

Massive capex — $30K–$2M per unit
Subject to US export controls
12–24 month deployment cycles
Vendor lock-in to single provider

SIBACUS

The Software Approach

Eliminate the multiply entirely. Decompose weights into bit-shifts and integer adds. Run on commodity ARM.

Zero hardware capex — software license only
No export restrictions — runs on ARM
Deploy in days, not months
Hardware agnostic — any ARM CPU

Head-to-Head Comparison

Side-by-side metrics across the five leading inference platforms.

Metric	NVIDIA GPU HARDWARE	Groq LPU HARDWARE	Google TPU HARDWARE	Cerebras HARDWARE	Sibacus Transform SOFTWARE
Cost / M Tokens	$1.50	$0.30	$0.80	$0.60	From $0.05
Power / Inference	300W	300W	200W	20kW system	1.5W–24W
Capex	$30K–$40K per GPU	$50M+ cluster	Cloud rental	$2M+ per system	Software license
Deploy Time	Weeks	Cloud only	GCP only	Months	Days
Upfront Cost	$10M–$100M+	$50M–$300M	Google-only	$5M–$50M	$0
Throughput	50–100 tok/s	500+ tok/s	50–80 tok/s	100+ tok/s	10–300 tok/s
Quality	Lossless	Lossless	Lossless	Lossless	Near-lossless (≤+0.27)
Export Restricted
Sovereign Ready

Key Differentiator

Same Throughput. Fraction of the Power.

Power is the #1 operational cost for hyperscalers — and the one they can't engineer away. Sibacus attacks it at the arithmetic level, delivering comparable throughput at 12–200× less energy per inference.

NVIDIA H100 GPU 50–100 tok/s

300W

Groq LPU 500+ tok/s

300W

Sibacus Performance 80–160 tok/s

12W

25× less power than GPU — comparable throughput

Sibacus Ultra 160–300 tok/s

24W

12× less power — exceeds GPU throughput

1.5W

Economy tier

200× less than GPU

Standard tier

50× less than GPU

12W

Performance tier

25× less than GPU

24W

Ultra tier

12× less than GPU

Power measured per inference thread on ARM Graviton4 (~1.5W/core). GPU baseline: NVIDIA H100 TDP 300W. Sibacus eliminates the multiply — bit-shifts consume orders of magnitude less energy than floating-point FMA.

Why Software-Defined Wins

Custom silicon optimizes the multiplier. Sibacus eliminates it.

33× Lower Cost

$0.05 vs $1.50 per million tokens compared to GPU baseline. Pure software — zero hardware capex.

6.6× Less Power

~2W per inference thread vs 300W per GPU. Same rack, fraction of the thermal footprint.

Deploy Anywhere

Runs on any ARM CPU — AWS Graviton, Ampere Altra, Raspberry Pi. No proprietary silicon required.

Sovereign Ready

No GPU export license. No foreign API dependency. Full data residency on domestic hardware.

Choose Your Service Level

One platform, four tiers. Scale from batch processing to real-time inference — all on commodity ARM hardware with zero upfront cost.

Economy

$0.05/M tok

1 core · ~10–20 tok/s

~1.5W per inference

Batch processing
Async APIs
Document analysis

33× cheaper · 200× less power than GPU

Standard

$0.15/M tok

4 cores · ~40–80 tok/s

~6W per inference

Enterprise chatbots
API endpoints
Interactive apps

10× cheaper · 50× less power than GPU

Popular

Performance

$0.30/M tok

8 cores · ~80–160 tok/s

~12W per inference

Real-time inference
Streaming responses
Production SLAs

Groq pricing · 25× less power · $0 capex

Ultra

$0.50/M tok

16 cores · ~160–300 tok/s

~24W per inference

Latency-critical
Financial services
Sovereign defense

3× cheaper · 12× less power than GPU

All tiers include: near-lossless quality (Δ+0.14), zero export restrictions, sovereign deployment, and OpenAI-compatible API.

See It For Yourself

Try the Sibacus Transform live in the Workbench, or request a benchmark against your production models.