MCA — MORPHIC CLUSTER ARCHITECTURE
The cost of serving AI no longer scales with model size.
A 70-billion-parameter model serves each token at the same speed as a 1-billion-parameter model, on consumer hardware.
USPTO Provisional · OEPM P202630407 · May 2026
§ 01 — Status quo
The entire AI economy rests on an assumption that no longer holds.
Bigger models require proportionally more infrastructure. Every doubling of model size doubles the cost per token served. The industry has been built on that linearity. Operational consequences:
Economic value concentrates at the top. The rest of the ecosystem competes on eroded margins over rented infrastructure.
§ 02 — Scale-invariant architecture
MCA breaks the linear relationship between model size and cost per token.
The change is architectural, not engineering. Two magnitudes the industry assumed were coupled now decouple:
Knowledge scales with size
A larger MCA model knows more, just like any current architecture.
Cost per token does not scale with size
A 70B serves each token at the same speed as a 1B on the same machine.
The same hardware — a consumer GPU costing €300–800 paired with a commercial CPU, or even a consumer CPU alone without any dedicated GPU — serves a small or a large model interchangeably. The serving economy is decoupled from model size.
HYBRID EMBODIMENT
The model body lives on CPU; the GPU only executes the vocabulary head.
MCA decomposes inference: the bulk of the compute runs on the CPU's DDR5 memory, and the GPU handles only the clustered LMHead. Because the GPU only ever sees the final projection, a consumer card with 8–12 GB is enough. VRAM never limits model size.
Transformer body · DDR5 · more memory means a larger model.
Clustered LMHead · 8–12 GB VRAM is enough · invariant to model size.
Running the entire model on GPU would be faster per token, but would require a data-center GPU with enough VRAM to fit 70 B in weights. The hybrid embodiment drops that cost without compromising recall.
§ 03 — Real measurements
Not projections. Measured tokens per second.
On off-the-shelf consumer hardware: AMD Ryzen 9 7900X + NVIDIA RTX 5070 Ti. Total ~€1,500.
| Model scale | Tokens / second | Recall vs. dense | Total hardware |
|---|---|---|---|
| 1,200 M parameters | 324 | 100% | ~€1,500 |
| 7,000 M parameters | under end-to-end validation | 100% | same envelope |
| 70,000 M parameters (projected) | ~150–200 | 100% | same envelope |
Recall = top-1 match with dense reference, verified by direct cross-check.
CPU-ONLY MODE
And it also runs without a dedicated GPU.
The same 1,200 M parameter model, executed only on a consumer CPU (AMD Ryzen 9 7900X + DDR5-5200 dual channel, no GPU), preserves reference quality.
CPU-only · 1.2 B · 100% recall
Bottleneck measured at 76% of peak DDR5 bandwidth. In concurrent multi-stream serving (16 streams, ~89% recall), aggregate throughput reaches 535–560 tok/s — useful for multi-tenant deployment.
REFERENCE
A dense 70B model on standard commercial DDR5 hardware would serve < 1 token per second due to memory-bandwidth constraints.
MCA is ~150–200× faster per token at the same scale.
tok/s · 1.2 B
tok/s · 70 B (proj.)
recall vs. dense
§ 04 — Economic implications
Three immediate consequences for the market.
The hardware barrier disappears
What today requires hundreds of thousands of dollars in data-center GPUs becomes servable on a sub-€2,000 machine.
Operating cost 100×–1,000× lower
For every token served, MCA consumes a fraction of the memory bandwidth, a fraction of the energy and a fraction of the hardware CAPEX of a dense model of equivalent capacity.
Erosion of the hyperscalers' moat
The "moat" of OpenAI, Anthropic and Google is built on the assumption that scaling AI requires their scale of capital. MCA invalidates that assumption at the architectural level, not the engineering level.
§ 05 — Unlocked verticals
Markets currently locked by serving cost.
When cost per token decouples from model size, four immediate fronts open up:
Edge AI
Large models running on-device: NUCs, SMB workstations, vehicles.
Sovereign AI
Public institutions, governments and regulated enterprises requiring on-premise AI for compliance.
Private AI
Law firms, banking, healthcare — data that cannot leave the perimeter.
Distributed AI
Inference federated across small nodes rather than centralized in a cloud.
§ 06 — Empirical validation
Reproducible, auditable validation program.
The project includes a documented cross-scale validation protocol:
Cross-scale validation 1.2B vs 7B
Monotonic convergence. The larger configuration achieves 1.001 nats lower cross-entropy at iso-tokens. No routing collapse, no dead clusters, no numerical divergence. Confirms the architecture learns better as it grows.
Multilingual validation
5 languages (English, Spanish, Russian, Chinese, Python code). 50,000 M tokens of pretraining.
Inference quality
100% top-1 match with the dense reference, verified by direct cross-check.
Hardware validation
Measured end-to-end on consumer-grade: Ryzen 9 7900X, DDR5-5200, RTX 5070 Ti.
Checkpoints, logs and SHA-256 hashes preserved. Auditable by third parties under NDA.
§ 07 — Intellectual property
Priority date established.
| Entity | Reference | Description |
|---|---|---|
| USPTO · provisional | May 2026 | Priority date established in the U.S. |
| OEPM · Spain | P202630407 · March 2026 | European coverage under the Paris Convention. |
| Claims | 19 | Core architecture, embodiments and variants. |
| PCT window | 12 months | Conversion to non-provisional international. |
Filed pro se, with no prior employer commitments and no co-inventors. Clean IP.
§ 08 — Implementation
Built for production. Not a paper.
- ›Go + C / CUDA. No Python, no PyTorch, no external ML frameworks.
- ›Standalone binaries linked only against libc, POSIX threads and the CUDA runtime.
- ›Bit-exact cross-stream inference: N concurrent decoders produce identical logits.
- ›Native Q8 quantization: weights in 8 bits with per-row scales, no detectable quality loss.
- ›Complete pipeline (training, inference, HTTP serving) compiles to lightweight executables.
Distance from the current repo to a production endpoint: weeks, not years.
§ 09 — Status and next phase
What's done, and what gets done with capital.
Today
- Architecture patented and validated cross-scale.
- Hybrid inference measured with full recall on consumer hardware.
- 70B embodiment projected under architectural invariants, pending end-to-end validation.
- Continuous multilingual 1.2B pretraining in progress (20 epochs, ~28 days wall-time).
Next phase
- End-to-end 70B validation on a rented B200/H100 pod.
- International PCT conversion within the USPTO 12-month window (EU, China, Japan, UK, Korea).
- Productization of the serving stack: authenticated HTTP, billing, observability, multi-tenancy.
- Team: 2–3 senior engineers to accelerate productization and hardening.
- GTM: 3–5 design-launch partners in defense, healthcare, regulated banking and government.
§ 10 — Timing
The AI inference market will exceed USD 200B annually before 2030.
The bulk of the value sits in serving, not training. All current infrastructure investment assumes serving with cost linear in model size. An architecture that breaks that linearity redefines the cost curve of the entire industry.
And the priority date is already established.
Contact
Technical due diligence · technical evaluation under NDA · live demonstration.
Direct response from the founder within 72 hours.