AI Inference Cost Benchmarks (2026)
Published operational data analyzing the VRAM requirements and token generation costs of standard FP16 architecture versus Lacesse's 1.58-bit Ternary architecture.
VRAM & Memory Footprint Comparison
| Model Architecture | Parameter Count | Required VRAM (Memory) | Hardware Requirement |
|---|---|---|---|
| Standard FP16 LLM | 7 Billion | ~14 GB - 16 GB | Enterprise GPU (A100/H100) |
| Lacesse Fikra Ternary (1.58-bit) | 7 Billion | ~3.5 GB - 4 GB | Consumer CPU or EdgeCore NPU |
Tokens-Per-Second (TPS) Throughput
Because 1.58-bit quantization replaces heavy matrix multiplication with simple addition, processing speeds scale dramatically on edge devices.
Standard FP16 Model (CPU Inference)
~5 TPS
Lacesse Fikra Ternary (EdgeCore NPU)
~45+ TPS
Benchmark FAQ
What is 1.58-bit quantization?
It is a mathematical breakthrough that reduces AI model weights to just three states (-1, 0, 1), dropping VRAM requirements by 70% and removing heavy matrix multiplication.
How does TPS affect AI application performance?
Tokens-Per-Second (TPS) dictates how fast an AI generates text. Fikra Ternary models achieve 45+ TPS on edge devices, enabling zero-latency conversational agents built with Fikra Claw.