Docs
MetaFuze FuZeLLM Whitepaper
This page hosts two documents you can publish directly from metafuze.ai: a full technical whitepaper and a two‑page executive brief.
Last updated: Jan 02, 2026
Deep dive on TrulyPrivate, persona packs, backend RRLM routing, operational controls, and benchmarking.
For security, ops, and budget owners. Defines TrulyPrivate and the savings model in plain language.
- Thin UI, smart router, server‑side policy enforcement.
- Multiple backend models, one consistent perimeter.
- Benchmarks as a product: quality, cost, latency, safety.
- TrulyPrivate: no shared control plane for the data path.
What you should publish (and why)
If you want MetaFuze to read as “serious infrastructure” instead of “another chat app,” publish the constraints you enforce and the measurements you optimize. That is your moat.
TrulyPrivate, what it means (and what it doesn’t)
“TrulyPrivate” is an operational posture: you decide the trust boundary and MetaFuze keeps the model execution, routing, telemetry, and audit artifacts inside it. The goal is to prevent shared control planes or hidden data paths from becoming accidental exfil channels.
- Dedicated tenancy boundary (on-prem FuZeBOX or private FuZeCLOUD tenancy).
- Server-side routing and policy enforcement (the UI is intentionally thin).
- Auditability: decisions, model calls, and outcomes can be logged inside your perimeter.
- Operational control over upgrades and configuration (especially for restricted networks).
- It’s not a promise that “nothing can ever leave” — it’s a design that makes egress explicit and governable.
- It doesn’t remove the need for normal controls (RBAC, key management, network ACLs, logging hygiene).
- It doesn’t imply one model fits all — routing exists because different tasks require different backends.
For buyers: define your boundary (air-gapped, VPC-only, or appliance), then validate that telemetry, prompts, and artifacts do not cross it except through explicit, auditable channels.
Benchmark methodology (no numbers)
MetaFuze treats measurement as a product feature. The RRLM router can score outcomes differently depending on persona/domain so you don’t confuse “good enough general answers” with “correct specialist answers.”
| Metric | Why it matters | How to measure | Typical guardrail |
|---|---|---|---|
| Task success | Quality signal per persona (coding ≠ medical ≠ exec) | Human accept, rubric score, or test harness pass/fail | No regression vs baseline |
| p50 / p95 latency | User experience and concurrency planning | End-to-end timing (router → backend → response) | p95 under target SLO |
| Tokens + GPU-seconds | Cost and capacity normalization across models | Backend usage + router aggregation per decision | Budget cap per request |
| Escalation rate | How often the router needs specialist consults | Count consult chain hops per conversation | Bounded by policy |
| Redo / correction rate | Proxy for “was this the right model?” | Follow-up sentiment, user correction, retry actions | Downward trend |
| Policy violations | Safety & compliance posture | Guardrail triggers, blocked responses, red-team prompts | Zero tolerance classes |
FuZeCORE: right-sized private inference
Even if a customer never uses multi-model routing, FuZeCORE is a competitive advantage: it delivers a cost-effective private endpoint by right-sizing the model (e.g., 7B–70B), precision profile (FP16/FP8/INTx where appropriate), runtime stack, and hardware configuration based on measured throughput and tail latency on the target class of GPUs.
- Customers don’t pay for a larger model or GPU tier “just in case”
- Runtime-agnostic: choose the best-fit serving engine per hardware class, not a single default
- Governance posture consistent with TrulyPrivate policies (egress bounds and audit-ready telemetry)
FuZeLABS: benchmarking + optimization program
FuZeLABS is the internal workflow that produces the right-sizing recommendation and validates optimizations. It continuously benchmarks model+runtime+hardware combinations so selection is based on measured outcomes, not vendor defaults.
- Workload suite and acceptance criteria by persona and domain
- Benchmark matrix: tokens/sec, p95 latency, stability under load, and cost signals
- System-level tuning (OS/kernel and CUDA configuration) validated in a harness
- Optional acceleration profiles, including cache-aware warm-start behavior where permitted by policy
The router can optionally feed
FuZeADMIN: customer-visible control plane, zero lock-in
MetaFuze does not trap customers behind a hidden vendor console. FuZeADMIN is exposed as part of the platform so operators can see nodes, runtimes, models, and benchmarks in their own perimeter, and make changes as their needs evolve.
- Model flexibility: swap models from a privately curated registry, or add approved models in your own registry
- Runtime flexibility: benchmark multiple serving engines (e.g., Ollama, llama.cpp, vLLM, Triton) with and without FuZe optimizations, then run the winner
- Upgrade path: start with a single 70B-class model on FuZeCORE, then later run two ~30B models side-by-side by enabling FuZeLLM routing
- Operator control: customers keep access to benchmarks, logs, and configuration so there is no vendor/model lock-in
Persona profiles shipped in the current RRLM config
This table is generated from the RRLM persona configuration (YAML .config files).
It lists the personas and their declared capabilities, without naming any underlying model endpoints or vendors.
| Category | Persona | Best for | Declared capabilities |
|---|---|---|---|
| Loading persona manifest… | |||
Note: backend selection (which models a persona is allowed to use) is configured per deployment and enforced server-side by the RRLM router. The marketing site intentionally does not publish deployment-specific endpoints.
Want this to read even sharper?
Publish your benchmark snapshot and a short threat‑model statement. Those two things answer the questions enterprise buyers ask in week one.