Run 750B+ Parameters On Local Consumer Metal.
LAYERIS breaks the GPU VRAM bottleneck. By orchestrating hardware-adaptive layer streaming across NVMe SSD, Host DDR5 RAM, and GPU VRAM, colossal foundation models execute seamlessly on your everyday workstation.
64 GB/s PCIe DMA burst loads layer weights into GPU memory. Tensor Core GEMM matrix multiplication computes forward pass and emits decoded tokens to TOK_OUT.
12.8 GB/s DirectStorage pipeline. Asynchronous double-buffering pre-stages Layer #49 into host RAM circular buffer while Layer #48 executes, hiding storage latency.
750B model weights divided into contiguous memory-mapped layer shards via io_uring / DirectStorage. Zero kernel-space duplicate copies.
How LAYERIS Unlocks 750B on 8GB GPUs
Traditional inference crashes with OOM because it attempts to load all 80+ transformer layers into GPU memory at once. LAYERIS transforms execution into a synchronized continuous stream.
Zero-Copy Fast Disk Shards
750B model weights are divided into contiguous, serialized layer chunks formatted with io_uring / Windows DirectStorage memory-mapping. Data is read directly without kernel space memory duplication.
Circular Sliding Layer Buffer
System RAM acts as a high-bandwidth reservoir. As Layer $N$ evaluates in the GPU, Layer $N+1$ and $N+2$ are already pre-fetched and prepared in pinned host memory, concealing PCIe transmission latency.
Micro-Buffer Ping-Pong Execution
Only 1 to 2 transformer layers occupy VRAM at any split millisecond. Once compute finishes for layer $k$, intermediate hidden activations are passed to layer $k+1$, instantly discarding previous weights.
Hardware Tier & Feasibility Planner
Input your local system specifications to find out how LAYERIS partitions a 750B, 405B, or 70B model across your physical hardware.
100% FEASIBLE WITH LAYERIS
Your system can run this 750B model without running out of memory via asynchronous 3-tier pipelining.
Model Stratum — Layer-Sliced LLMs
Download whole models or modular layer slices. Tailored for instant execution with the LAYERIS runtime.
Get Started with LAYERIS in 3 Steps
Zero complex CUDA compilation. Install our high-performance runtime daemon and start serving models locally or through an OpenAI-compatible endpoint.
OpenAI Compatible
Drop-in replacement for api.openai.com. Connect directly to LangChain, LlamaIndex, OpenWebUI, or Continue.dev.
Auto Layer Calibration
Dynamically re-balances VRAM vs RAM allocation if other applications start consuming memory on your PC.
Modular Layer Slicing
Download individual layer packs. Stop, resume, or mirror specific transformer stages across external drives.
LAYERIS vs Traditional Inference Engines
See how layerwise tiered offloading compares with standard vLLM and llama.cpp when tackling 700B+ models.
| Architecture Feature | Standard vLLM / HuggingFace | Standard llama.cpp | LAYERIS Runtime |
|---|---|---|---|
| Min VRAM for 750B (Q4) | 420+ GB (8x H100) | Whole-model RAM required (~450GB) | 8 GB - 16 GB VRAM (Any consumer GPU) |
| Memory Architecture | Monolithic GPU Load | CPU RAM to GPU Offload | 3-Tier Continuous Layer Streaming |
| OOM Crash Frequency | Instant Crash if VRAM exceeded | Frequent on long context | 0% (Guaranteed mathematically) |
| Disk Transfer Bottleneck | Full cold load (15-30 min) | Full model mmap freeze | Async Double-Buffer Zero-Stall DMA |
| Consumer Hardware Cost | $300,000+ Enterprise Cluster | $8,000+ High-RAM Workstation | $1,200 Workstation / Existing PC |