Same outputs. Significantly lower GPU cost. Additive to vLLM, KV cache, and prefix caching. No model changes. No retraining. Available now.
How it works
Gate fail at any stage → full forward pass runs. No degraded outputs. Ever.
Integration
Navyra wraps your existing HuggingFace model at the forward pass level. One call. Your existing serving stack — vLLM, TensorRT-LLM, SGLang — continues to run exactly as before. Navyra adds on top.
# Your existing setup model = AutoModelForCausalLM.from_pretrained(MODEL) tokenizer = AutoTokenizer.from_pretrained(MODEL) # Add Navyra import navyra engine = navyra.activate(model, tokenizer, api_key=KEY) # Everything else unchanged output = engine.generate(prompt)
Stack position
Navyra is Layer 3 in the inference optimisation stack. Every layer is additive — gains compound, not replace.
Navyra doesn't replay activations blindly. A three-stage verification gate — Hamming pre-filter, cosine similarity, activation trajectory confirmation — checks each request before any bypass occurs. Gate fail = full forward pass, always. This is why Navyra achieves 0.9999 fidelity where comparable research approaches degrade.
Validated results
Tested on Mistral-7B · NVIDIA Quadro RTX 6000 (24GB) · CUDA-synchronised GPU timing · Baseline: vLLM + KV cache enabled · Stable across 18 parameter configurations.
Measured with CUDA-synchronised GPU timing. Genuine layer bypassing — not software overhead reduction. Additive to vLLM and KV cache; gains compound on top.
Output on cache hits is indistinguishable from full inference. 100% Top-1 token match on every cache hit across all 18 configurations tested. The gate does not let degraded results through.
Navyra fires on semantically clustered domain traffic and stays silent on diverse queries. 6.3× selectivity ratio between domain and mixed traffic. It does not fire when it should not.
The activation cache lives entirely in CPU RAM. Zero GPU VRAM consumed. Cache saturates at approximately 52 entries within the first 100 requests. No infrastructure overhead.
Where it works hardest
Navyra's efficiency gain is proportional to the semantic clustering of the workload. Domain-specific, high-volume deployments see the largest savings. The cache fires on what it should and stays silent on everything else.
Estimates based on benchmark workload. Actual hit rate depends on traffic distribution.
KV cache helps one user in one session. Prefix caching helps users who share an identical system prompt. Navyra helps every user who asks a semantically similar question to any previous user — across all sessions, across all users, simultaneously. The more your users ask variants of the same questions, the greater the gain.
Domain traffic vs diverse traffic. Navyra fires correctly and stays silent correctly.
The activation cache persists across restarts via cache_dir. Hit rate compounds over time as the cache settles on the most common activation patterns for your specific deployment. Day-one performance is the floor, not the ceiling.
Use cases
Any high-volume, domain-specific, self-hosted LLM deployment. Privacy-mandated or cost-driven — no API alternative. The tighter the semantic clustering, the greater the saving.
The additive optimisation layer
GPU cost scales with every request.
Most of that compute
has already been done.
KV cache and prefix caching solve real problems. Neither addresses redundant foundational reasoning firing across separate user requests at the activation layer. Navyra is the third layer — additive to both, requiring nothing from either. Patents pending.
Get started
Free 30-day trial on your own infrastructure. See your actual saving on your real traffic. At the end of the trial, move to our pay-per-saving model. No saving, no charge. Ever.
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