A native compression engine for long-context inference, with exact fallback and serving-framework adapters. Historical vLLM-compatible gates validated token-for-token capacity uplift; current work keeps those compatibility shims separate from the native path.
Most compression is a trade. Quantize the weights. Drop the precision. Accept the drift. The model gets smaller. The answers change.
Sfiniti AI is a different kind of compression — a native cache engine with exact fallback and named validation gates. Historical serving-framework gates preserved token-for-token output; current development keeps compatibility shims separate from the native execution path.
Run more inference. Preserve the output.
Historical page-authoritative exact-output validation gate on Qwen 7B at 128K. Newer private work focuses on native execution and measured quality routes, with exact fallback outside admitted envelopes.
Most public alternatives report compression with perplexity, accuracy, or quality-regression metrics. Our public claim stays bounded to named validation gates and separates exact-output historical evidence from newer native quality-compression work.
| Method | Compression | Output guarantee | Validated scale |
|---|---|---|---|
| Sfiniti AI | Historical exact-output gates up to 4.2× page-authoritative (Qwen 7B, 128K) 2.058× multi-request serving-framework gate · 1.78×–1.95× concurrency at K64/V64 (32K–128K) · up to 3.2× page-level on 72B H200 |
Token-exact (validated gates) | 7B–72B (72B at TP=2) |
| GEAR | up to 2.29× peak-memory reduction | Near-lossless (perplexity) | 7B-13B |
| TurboQuant | ~4× at 3.5 bits | Near-neutral perplexity | 70B class |
| vLLM FP8 | 2.0× | Sub-1% perplexity delta | 70B+ |
| KIVI | up to 4× (2-bit) | Quantization loss | 7B-70B |
| H2O | up to 4× | Eviction loss | 7B-13B |
Token-for-token match against the uncompressed baseline in named exact-output H200 gates.
Qwen2.5-72B validated with tensor-parallel = 2 on NVIDIA H200, including production-style ragged batches.
Native engine with HuggingFace, MFabric, and serving-framework adapters under development. No retraining or fine-tune required.
Clinical decision support and diagnostic assistance under FDA, MDR, and HIPAA frameworks. Output reproducibility is a regulatory boundary, not a nice-to-have.
Compliance review, automated underwriting, and regulated advisory. Every inference call must be reproducible for audit. Quantization-induced drift breaks the audit trail.
Contract review, eDiscovery, and any deployment under the EU AI Act. Reproducibility is not optional. Same input must produce same output, traceable to a fixed model.
For inference teams in regulated and high-fidelity workloads.