The Crisis of Idle Intelligence
We are currently building AI infrastructure with a fatal flaw: Memory Fragmentation.
If you want an AI agent to respond to a user instantly, those weights must live in GPU HBM (VRAM). But VRAM is the most expensive real estate on earth. A single H100 with 80GB of VRAM costs ~$40,000. If you use it to host a 70GB model, that GPU is "full." It can serve exactly one agent.
The Economic Deadlock
If that agent is only talking 1% of the time, you are wasting 99% of a $40,000 asset.
The alternative is the "Cold Start": you keep the model on a cheap NVMe SSD. When the user speaks, you load it.
Loading 70GB model from SSD → GPU: 10-30 seconds
Result: In a world of instant gratification, a 10-second delay is a product killer.
The $168 Billion Market
For the last 3 years, the money was in Training (building GPT-4). By 2026, the money flips to Inference. The market is projected to hit $168 Billion by 2030.
"Most inference GPUs run at 15-20% utilization. The other 80% is wasted heat because the model must sit in VRAM just in case the user speaks."
Real Performance (Verified on Hardware)
Tesla T4 (Production Verified)
Engineering Deep Dive
We don't just "optimize" loading—we completely bypass the standard OS memory stack. Here is the actual architecture of the Zynta Engine.
The Unpageable Arena
Bypassing the OS Kernel VMM
Standard malloc or even cudaHostAlloc triggers complex OS paging mechanisms. When you request 40GB, the OS lazily allocates pages, causing thousands of page faults (soft & hard) during the copy process.
// Zynta (Instant)mmap(..., MAP_LOCKED | MAP_POPULATE); // Force physical RAM mappingcudaHostRegister(...); // Pin pages for DMA engine
We pre-allocate a massive "Arena" at boot time. This memory is physically pinned (locked) in RAM. When a model request comes in, we don't allocated memory—we just hand the DMA engine a pointer to this pre-existing, hot, physical address range. Zero allocations. Zero page faults.
Layer Pipelining
Hiding the PCIe Bandwidth Limit
Physics dictates that PCIe Gen3 x16 is limited to ~16GB/s. You cannot fix this with software. However, you don't need the last layer of the model to compute the first token.
We built a custom scheduler that treats model layers as a dependency graph. The CUDA kernels for Layer N launch the exact microsecond the DMA transfer for Layer N completes, while Layer N+1 is still transit.
The Unitary Ghost
Speculative Decoding on Steroids
Waiting 1.8s for the full T4 load is still too slow for voice. We keep a tiny, 4-bit quantized "Ghost Model" (e.g., Llama-7B-4bit or smaller) permanently resident in the 1GB VRAM headroom.
- User speaks → Ghost Model answers immediately (11ms).
- Main Model loads in background (1.8s).
- Once Main Model is hot, we swap the KV cache and continue generation with full fp16 precision.
This creates the illusion of instant wake-up for a massive model, while strictly adhering to the memory budget.
Benchmarks vs Competition
Hardware Disparity Note
Our benchmarks are run on Tesla T4 (2018) cards—very old, slow GPUs compared to the H100s used by competitors. We use T4s strictly because we lack the resources to rent modern clusters. Despite the 5-generation hardware gap, our software architecture bridges the performance difference.
| System | Cold Start | Warm Start | Hardware | Cost/hr |
|---|---|---|---|---|
| Modal | 10-30s | 40ms | H100 | $3-5 |
| Replicate | 5-15s | 40ms | A100 | $1.50 |
| Together.ai | 1-3s | 20ms | A100 cluster | $0.80 |
| Groq | 50ms | 10ms | Custom LPU | N/A (SaaS) |
| Zynta (T4) | 1.8s | 24ms | T4 | $0.30 |
Transparency & Limitations
- Verified ONLY on Tesla T4
- We have NOT tested on large models (70B+) or H100s due to lack of compute resources.
- Single active model per GPU
Call for Compute
We have proven the architecture on T4. However, we are blocked from scaling because we lack the hardware to test on modern datacenter GPUs (A100/H100).
We are looking for infrastructure partners to sponsor compute. Let's build the most efficient inference stack together.
Strategic Infrastructure Partnership
We are scaling toward 405B+ parameter benchmarks. We are currently open to strategic partnerships with compute providers and venture groups to accelerate our infrastructure roadmap.
Direct inquiries to ashishrathour1102@gmail.com