Why Revyzor?
The local LLM landscape is maturing fast, but it still lacks the compliance, governance, and cost management capabilities that regulated enterprises require. Revyzor closes that gap.
Local AI is powerful. It is not yet governed.
No record of what the AI saw
When a regulator asks “show us what this AI was working with six months ago,” there is no standard answer. The model’s working memory vanishes the moment the process ends. No audit trail. No chain of custody. No evidence.
Every restart destroys conversation state
A server restart, a scale event, a maintenance window: every active conversation is destroyed. Users start over. The AI re-reads every document from scratch. Hours of context, gone.
Compliance blocks deployment
The General Counsel won’t sign off on AI for live deals because nobody can show what the model relied on, that its memory is intact, or that the process is auditable. AI stays limited to internal work. Deal partners draft by hand.
Agentic AI has no governance at all
AI agents chain 10–90 steps autonomously, reading documents, calling tools, making decisions. If step 23 of 50 makes a bad decision, there is no way to show what the agent knew at that point. Guardrails govern what agents can do. Nothing governs what they did.
How Revyzor closes it.
Compress and save the model’s reasoning state after every interaction. A 5,097-token session restores in 56.2 milliseconds instead of 417 to re-read the document, hours, days, or months later.
Every saved state carries a cryptographic credential: which model, which organisation, which document, when. Cryptographically signed: change one byte and the signature fails verification. An auditor can check it without accessing the underlying data.
Lossless compression: what you reload is byte-for-byte what was captured. The same state, six months later, exactly as it was. A record you can stand behind, not a log file.
The economics come included.
You buy Revyzor for the record it keeps. These are the added benefits.
How the numbers relate.
Different figures on this site measure different things. Here is how they fit together.
Lossless compression of live GPU state: 1.444x to 1.52x
This is the ratio on the working state held in GPU memory during serving, with output bit-for-bit identical to running uncompressed. 1.444xis the figure measured on real Qwen 2.5 7B state under vLLM 0.19–0.21; 1.50–1.52xon vLLM 0.22.0’s new tensor layout. Both bf16. A higher-ratio mode exists for workloads that do not require exactness. It is opt-in and never the default.
Session restore: 56.2 ms against 417 ms
Measured end to end on a 5,097-token contract, Qwen 2.5 7B bf16, L40S, local NVMe: 33.5 ms of disk read, 7.9 ms host-to-device, 15.3 ms decompression. Re-reading the same document from cold on the same machine takes 417 ms. Restore latency scales with session size and with the speed of the storage path, so treat these as figures for that configuration rather than as constants.
This is the comparison for a session that is no longer in GPU memory. A session still resident in the serving engine’s own prefix cache is faster than either, at 39.5 ms, and costs nothing. Revyzor is for the state that has been evicted, or that has to survive a restart, or that has to be produced again months later.
Latest validated findings.
vLLM 0.22.0 Compatibility (June 2026)
vLLM 0.22.0 changed the internal KV cache tensor layout from a 5D paged structure to a 3D flat structure, with no migration guide and no deprecation warning. Revyzor detected the change, adapted the connector, and validated lossless compression at 1.50–1.52xacross all 36 layers of Qwen 2.5 3B on A100 SXM4. The connector now supports both layouts with runtime detection. Existing deployments on vLLM 0.19–0.21 continue to work unchanged.
Model Architecture Detection
Not all models store KV cache the same way. DeepSeek V2 and V3 use Multi-Latent Attention (MLA), which stores a compressed latent representation instead of raw key-value pairs. Revyzor automatically detects the attention architecture (GQA, MQA, MHA, or MLA) from the model configuration and reports compatibility before any GPU time is spent.
Validation Details
Compression ratios: 1.444x (vLLM 0.19–0.21), 1.50–1.52x (vLLM 0.22.0). bf16 dtype. GPU-native.
Models tested: Qwen 2.5 3B/7B/14B/27B, Gemma 12B. Architectures: GQA (all standard attention models).
GPUs tested: A100 SXM4 80GB, A100 PCIe 80GB, L40S 45GB, H200, RTX 4000 Ada.
Frameworks: vLLM 0.19.1, 0.20.0, 0.22.0. SGLang. NVIDIA NIM.
MLA models (DeepSeek V2/V3): detected and flagged automatically. Compression integration in development.
All benchmark scores (LongBench v2, NIAH) independently reproducible on public models and standard hardware.
Make every AI decision defensible.
We run Revyzor on your workload and you see the results before committing to anything.
Request a Pilot