Serve 100 Large AI models on a single GPU with low impact to TTFT
Details
- External ID
- 45861326
- Source
- HN
- Company
- —
- Product
- Serve 100 Large AI models on a single GPU with low impact to TTFT
- Website domain
- github.com
- Launched
- Nov. 8, 2025
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.37882096069869
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon.With this project you can hot-swap entire large models (32B) on demand.Its great for:Serverless AI InferenceRoboticsOn Prem deploymentsLocal AgentsAnd Its open source.Let me know if anyone wants to contribute :)
Enrichment
- Theme
- lightweight and on-device AI runtimes
- Vertical
- Horizontal
- Function
- Model & infra
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- serve multiple large ai models on single gpu
- Manually corrected
- False
Could you build this?
No Serving 100 large models on one GPU with low TTFT requires deep systems research in GPU memory management, custom CUDA kernels, PCIe/NVMe direct transfer pipelines, and model weight paging.
What it would actually take: Requires deep C++/CUDA systems engineering, custom paging and weight-streaming architectures (similar to vLLM/DeepSpeed or GPUDirect Storage), and advanced operating system memory virtualization. A developer must write low-level kernel drivers and asynchronous GPU copy routines while benchmarking sub-millisecond TTFT under heavy contention.
Discussion
1 comment analyzed.
Concerns raised: GPU memory limitations for large models
Feature requests: Hot-swap model portions to run sequentially on limited GPU memory, Split model inference across multiple GPU memory loads
Competitors
Other products that read as similar to this one — 337 launches clear the similarity bar, closest 8 shown.
Attention rank: #224 of 338 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 4 days after the earliest competitor.
- Moonshine Open-Weights STT models · hn · 2026-02-24 · 316 upvotes · similarity 0.51
- Lifeboat, 2-6x more concurrent agent sessions per GPU, no quantization · hn · 2026-09-23 · 6 upvotes · similarity 0.49
- ZeroGPU · ph · 2026-06-09 · 308 upvotes · similarity 0.49
- SF Tensor - Infrastructure for the Era of Large-Scale AI Training ⚡ · yc · 2025-11-04 · 23 upvotes · similarity 0.49
- Cuts Long Horizon Inference Costs by 50% via external KV Cache Offload · hn · 2026-07-26 · 22 upvotes · similarity 0.49
- General Compute · ph · 2026-05-22 · 314 upvotes · similarity 0.48
- Lamb Labs: Custom Chips for AI Inference · yc · 2026-08-03 · 64 upvotes · similarity 0.48
- Token Economics Calculator for AI inference hardware · hn · 2025-11-19 · 13 upvotes · similarity 0.47
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a model & infra tool for Fintech yet.