Execution control foundation
Universal gateway, workload characterization, context/compute/math leases, Planner v4, provider and strategy selection, verification and learning.
AI Fabric Control · Autonomous Execution Intelligence & Control
Control the AI Fabric. Use only the compute the outcome requires.
MMTNEXUS OPT is an autonomous AI Fabric Control layer. It decides in real time how each workload should consume context, models, runtimes, memory, GPU compute, routing and energy; then executes, measures and verifies the required outcome without changing model weights.
Limited-time public beta. One MMTNEXUS account across the portfolio. Fair-use and provider-specific limits may apply. Enterprise enquiry →
Workload execution intelligenceCharacterize the task, required outcome, uncertainty, latency, cost and energy objective before execution.
Context, compute & math leasesAllocate only the bounded context, compute and mathematical work required by the workload.
Hardware-aware planningUse GPU/VRAM telemetry, KV-cache behavior, hardware class, queue state and capability gates when available.
Runtime & precision policySelect eligible runtime, batching, concurrency, BF16/INT8 precision and multi-GPU strategies within quality gates.
Outcome verificationReject savings that violate meaning, quality, policy or SLA; escalate or re-run only what is required.
Closed-loop learningPersist tenant-, workload-, model- and hardware-specific observations to improve the next safe plan.
Execution control loop
Understand. Plan. Execute. Verify. Learn. Re-plan.
The serving engine performs inference; OPT decides how the workload should execute and accepts efficiency only when the required outcome is preserved.
UnderstandEstimatePlanExecuteVerifyLearn
Implemented platform status
Execution control validated in the lab. Production validation continues.
The core control loop covers workload characterization, resource prediction, execution planning, measurement, outcome checks and bounded learning. Verified Intelligence research for planner quality and independent customer-like validation remain separate workstreams—not certified production claims.
Measured and policy-aware
A40 and L40S physical-GPU measurements, GPU/VRAM and energy telemetry, BF16/INT8 quality gates, and B300 autonomous closed-loop lab research. Broader fleet validation remains pending.
Scale and coverage roadmap
Planner v2 verified-intelligence research targets evidence-to-claim alignment and independent outcome review. Fleet scale, multi-GPU control and production statistical gates remain expansion priorities.
Progressive integration
MODEL WEIGHTS UNCHANGEDOPT Gateway → SDK / Runtime → AI Provider → GPU Cloud / Control Plane → OEM
Fresh internal live-routing certification
Production VPS → signed AI Fabric → NVIDIA L40S: 111/111 exact.
A staged 10% → 25% → 50% → 100% engineering routing campaign passed all four gates with 111 exact protected outcomes from 111 executions and zero non-exact results. A fresh 30/30 signed production-path certification after persistent HTTPS and indexed occupancy control measured 11.58 ms mean GPU worker execution, 700.14 ms mean dispatch-to-completion, 651.33 ms median and 698.49 ms P95. This is 19.36% lower than the prior certified signed path and 66.48% lower than the original pre-keepalive path, while preserving 100% exactness.
Progressive routing gates
10% → 25% → 50% → 100%
Exact protected outcomes
Zero non-exact executions in the staged campaign.
Mean signed dispatch-to-completion
Measured separately from the signed control-plane lifecycle.
Claim boundary: internal MMTNEXUS engineering traffic from the production VPS through the authoritative signed Fabric to an enrolled Vast.ai NVIDIA L40S worker. This is not third-party independent validation and is not claimed as genuine external customer production traffic.
Same-L40S strict paired all-KPI proof
NORMAL vs OPT on the same physical L40S · 50 strict quality-valid pairs.
On the 50 pairs where both NORMAL and OPT returned every required exact value, OPT reduced input tokens by 97.32%, GPU compute time by 32.56%, average power by 8.66%, energy per request by 39.20%, and operational CO₂e per request by 39.20% at the same measured Taiwan grid intensity. OPT itself was exact on 60/60 executions; NORMAL was exact on 50/60. Peak VRAM was unchanged.
Input tokens
5,844.8 → 156.8
GPU compute time
808.8 ms → 545.4 ms
Average GPU power
221.34 W → 202.17 W
Energy / request
0.04910 Wh → 0.02985 Wh
Operational CO₂e / request
0.02538 g → 0.01543 g · 517 gCO₂e/kWh
OPT exact outcomes
Strict savings use only the 50 pairs where both arms were exact.
Claim boundary: controlled synthetic engineering A/B on the same physical Vast.ai NVIDIA L40S and the same resident Qwen2.5-3B-Instruct BF16 model. Savings are calculated only on 50 strict quality-valid pairs. Carbon uses a fresh trusted Electricity Maps Taiwan intensity overlapping the campaign. This is not third-party independent validation or genuine external-customer production calibration.
L40S concurrency scaling proof
At batch 32: 8.67 → 59.80 req/s with 86.40% lower energy/request.
A same-L40S NORMAL vs OPT batching campaign tested concurrency 1, 4, 8, 16 and 32 with two repeats per level and no OOM. At batch 32, OPT increased throughput by 589.76%, reduced batch latency by 85.50%, input tokens by 91.24%, average power by 6.21%, and energy plus operational CO₂e per request by 86.40%.
Throughput
8.67 → 59.80 req/s
Batch latency
3.691 s → 0.535 s
Input tokens
1,481 → 129.75
Energy / request
0.00986 Wh → 0.00134 Wh
Average GPU power
307.77 W → 288.66 W
Capacity gate
Successfully tested through batch 32.
Claim boundary: controlled synthetic engineering batching test on the same physical Vast.ai NVIDIA L40S and resident Qwen2.5-3B-Instruct BF16 model. NORMAL and OPT had equal exactness at every level: 100% at batch 1 and 75% at batches 4/8/16/32. These are quality-parity scalability results, not universal 100% exactness. Carbon uses same-window trusted Electricity Maps Taiwan intensity of 516 gCO₂e/kWh.
Current long-context optimizer proof
9,725 → 166 estimated input tokens · 98.29% reduction.
A fresh duplicate-RAG workload executed through the current production optimizer removed 237 exact duplicate RAG blocks while retaining 100% of protected evidence. Optimizer processing completed in 38 ms and selected ADAPTIVE.
Estimated input reduction
9,725 → 166 · 9,559 tokens avoided
Exact RAG blocks removed
Deterministic exact-block de-duplication.
Evidence retention
Protected evidence guard passed with full retention.
Claim boundary: controlled synthetic long-context duplicate-RAG workload through the current production optimizer. This proves optimizer-layer context/token-estimate reduction and evidence retention; it does not by itself prove provider-billed token savings or genuine external customer production savings.
Control domains
A workload governor from API request to runtime and hardware policy.
Workload intelligence
Classify workload, required outcome, difficulty, uncertainty and the minimum sufficient work before execution.
Model & provider intelligence
Select an eligible provider, model tier and failover policy without locking OPT to one vendor.
Execution intelligence
Generate and compare plans across context, compute, math, cache, batching, concurrency, replay and escalation.
Infrastructure intelligence
Use GPU class, VRAM, KV cache, precision, runtime, topology, energy and readiness telemetry within supported controls.
API integration
Verify meaning, output quality, policy and SLA; stop, escalate or replay only the required stage when proof fails.
Gateway, SDK & OEM
Deploy as an enterprise gateway, embedded SDK/runtime, provider control layer, GPU-cloud control plane or OEM capability.
MMTNEXUS Lab Validation · Multi-Provider AI
Integration-tested across 7 leading AI provider routes.
AI Fabric Control publishes measured and certified workload-control results for OpenAI, Anthropic, Google Gemini, DeepSeek, Mistral, xAI and OpenRouter. Select a provider to see its measured result, workflow and limitations.
CERTIFIED
OpenAI
GPT-5.6 Sol17.91%measured token reduction
363→298before / after
Outcome Guard 100% · critical literals retained 100%
View OpenAI proof →CERTIFIED
Anthropic
Claude Sonnet 522.17%measured token reduction
627→488before / after
Outcome Guard 100% · critical literals retained 100%
View Anthropic proof →CERTIFIED
Google Gemini
Gemini 3.8 Flash18.21%measured token reduction
379→310before / after
Outcome Guard 100%
View Google Gemini proof →CERTIFIED
DeepSeek
DeepSeek V4 Flash17.14%measured token reduction
391→324before / after
Outcome Guard 100%
View DeepSeek proof →CERTIFIED
Mistral
Mistral Small Latest17.04%measured token reduction
399→331before / after
Routing verified · token certification certified
View Mistral proof →CERTIFIED
xAI
Grok 4.66.54%measured token reduction
994→929before / after
Routing verified · token certification certified
View xAI proof →CERTIFIED
OpenRouter
OpenAI GPT-5.6 Sol17.91%measured token reduction
363→298before / after
Outcome Guard 100% · Evidence Guard 22/22 · critical literals retained 100%
View OpenRouter proof →Multimodal lab coverage
Explore AI Fabric ControlGoogle Gemini: image + video · OpenAI: image · xAI: image + video · OpenRouter: image + video · Mistral: image generation.
MMTNEXUS LAB VALIDATED means tested by MMTNEXUS in its own integration and certification environment. Vendor names identify tested technologies and do not imply endorsement, sponsorship or partnership.
MMTNEXUS OPT · Validated Execution Intelligence
Advanced optimization capabilities, summarized for public release.
MMTNEXUS OPT has progressed beyond token reduction into a broader execution-control fabric for AI workloads. The platform can analyze workload requirements, minimize unnecessary context and compute, select bounded execution strategies, coordinate provider and infrastructure choices, verify required outcomes, and learn from measured runtime evidence. Internal validation spans multilingual and enterprise workloads, multi-provider execution, context and memory control, agent and tool workflows, structured data, multimodal surfaces, hardware-aware planning, resource scheduling, quality-constrained precision, and governed re-planning.
Detailed benchmark values, provider-specific selections, internal execution recipes, pricing assumptions, telemetry, hardware-control thresholds, certification ledgers and experimental evidence are intentionally withheld from the public product page. They remain available only for controlled technical evaluation, qualified customer discussions, independent verification and other authorized review under the appropriate confidentiality framework.
Public claim boundary: MMTNEXUS describes capabilities and validated progress at a high level here. Detailed internal evidence is not published on this page and should not be interpreted as independent third-party certification unless explicitly stated.
Developer Downloads
Install the gateway or integrate the SDK.
One optimization core, three integration paths. Downloads are available to registered MMTNEXUS users and are protected with short-lived signed links.
Register or sign in to unlock downloads.
Version 1.0.0
Token Saver for macOS
Zero-code local gateway for agents and API applications. Apple-notarized Universal build for Apple Silicon and Intel.
Verify download
SHA-256
5760bbf1843dbb0dc666bf096ffb18d06d22b79f4a9821c2a491fcd10be0bc26Version 0.1.3
Python SDK
Embed MMTNEXUS optimization directly into Python agent and application workflows.
Verify download
SHA-256
847ec3825bcc9e2aaa32edc36d7994077a87a8c11711e2b21b0f11eef045403dVersion 0.1.0
TypeScript SDK
Integrate the same guarded optimization layer into TypeScript and Node.js applications.
Verify download
SHA-256
31a4d0896393dd86c1dce233f12f528bae366ad90fdefb19f83f06178fcdb9b5Free Public Beta
Control how your AI workloads execute—and prove the outcome.
Start with gateway optimization today, then expand into provider, runtime and infrastructure control as telemetry and authority become available.