AI · alternatives · 2026

24 alternatives to An RDMA/Infiniband Distributed Cache for Fast Inference and Training

A high-performance RDMA distributed file system for fast LLM Inference and GPU Training. - GitHub - blackbird-io/blackbird: A high-performance RDMA distributed file system for fast LLM Inference and GPU Training.

This is a distributed cache system designed for machine learning applications, built on RDMA and Infiniband technology. It serves developers and researchers who need fast data access during model inference and training… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
Tiny-vLLM – high performance LLM inference engine in C++ and CUDA▲205
Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM - jmaczan/tiny-vllm
May 2026 · github.com · its alternatives →
2
Speeding up LLM inference 2x times (possibly)▲419
Here's a project I've been working on for the last few months. It's a new (I think) algorithm, that allows to adjust smoothly - and in real time - how many calculations you'd like to do during inference of an LLM model. It seems that it's possible to do just 20-25% of weight multiplications instead of all of them, and still get good inference results. I implemented it to run on M1/M2/M3 GPU. The mmul approximation itself can be pushed to run 2x fast before the quality of output collapses. The inference speed is just a bit faster than Llama.cpp's, because the rest of implementation…
2024 · asciinema.org · its alternatives →
3
General Compute▲315
AI models that run on an inference cloud optimized for speed
May 2026 · generalcompute.com · its alternatives →
4
Cuts Long Horizon Inference Costs by 50% via external KV Cache Offload▲22
Hey HN, we’re the developers of OpenLake, an open source storage engine for offloading LLM KV caches from GPU memory into a shared tier of RAM and NVMe. We built OpenLake because KV caches are outgrowing GPU memory. A single 256K token conversation on Gemma 4 31B produces approximately 43GB of KV state, more than half the memory of an 80GB H100. The problem becomes even harder across a cluster: a prefix cached on one GPU host is unavailable when the next request lands on a different GPU, forcing the new GPU to repeat work the fleet has already completed. Once the KV cache is offloaded,…
Jul 2026 · github.com · its alternatives →
5
We Beat MLPerf: Modern Storage for KV Offload and LLM Training▲35
High performance storage engine for efficient LLM inference and GPU Training.
Sep 2026 · theopenlake.com · its alternatives →
6
Initial release of the distributed TensorFlow runtime▲85
An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow/core/distributed_runtime at master · tensorflow/tensorflow
2016 · github.com · its alternatives →
7
We made our own inference engine for Apple Silicon▲186
We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
2025 · github.com · its alternatives →
8
Inference Engine by GMI Cloud▲180
Fast multimodal-native inference at scale
Dec 2025 · gmicloud.ai · its alternatives →
9
Fine-tune an 8B model on a 4 GB laptop GPU▲139
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU. - MakazhanAlpamys/Soup
Aug 2026 · github.com · its alternatives →
10
Pip install inference, open source computer vision deployment▲71
Deploying vision models is time consuming and tedious. Setting up dependencies. Fixing conflicts. Configuring TRT acceleration. Flashing (and re-flashing) NVIDIA Jetsons. A streamlined, developer-friendly solution for inference is needed. We, the Roboflow team, have been hard at work open sourcing Inference, an open source vision deployment solution. Our solution is designed with developers in mind, offering a HTTP-based interface. Run models on your hardware without having to write architecture-specific inference code. Here's a demo showing how to go from a model to GPU inference on a video…
2023 · github.com · its alternatives →
11
KiviDB, a faster Redis-compatible memory database and vector cache▲68
A fast, multi-threaded Redis® and Valkey alternative written in Rust: drop-in compatible, with full-text and vector search built in - kividbio/kividb
2d ago · github.com · its alternatives →
12
Free Inference Engineer and Model Training Roadmap▲16
Free, gamified roadmaps for LLM engineering: an Inference Engineering path (KV caches, CUDA kernels, production vLLM serving) and a Model Training path (pretraining on a budget, scaling laws, SFT/DPO/GRPO) — 185 tasks with auto-verified milestones instead of a paper certificate.
Aug 2026 · inferquest.org · its alternatives →
13
Dockerized GPU Deep Learning Solution (Code and Blog and TensorFlow Demo)▲80
An on-premises, bare-metal solution for deploying GPU-powered applications in containers - emergingstack/es-dev-stack
2016 · github.com · its alternatives →
14
50+ LLMs on 2 GPUs with 2-Second Swapping? We built AI-Native Runtime▲5
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com · its alternatives →
15
Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
16
Vector Cache▲9
A Python Library for Efficient LLM Query Caching
2024 · github.com · its alternatives →
17KC
KVBoost – chunk-level KV cache reuse for HuggingFace, 5–48x faster TTFT▲20
If you’ve ever been curious about how GPT actually works under the hood, I built a small project you might find interesting. I implemented a GPT-style transformer from scratch in a single notebook—covering tokenization, embeddings, causal self-attention, training, and autoregressive text generation without relying on high-level abstractions. The focus was on mechanistic clarity rather than scale or performance, and the notebook is structured to read more like a technical walkthrough than an experiment log. Feedback from people who’ve built or studied transformers would be very welcome.
May 2026 · pythongiant.github.io · its alternatives →
18
Local fine tuning for Mistral and SDXL, GPU mem/latency optimization▲36
100% bootstrapped new startup. It lets you fine tune Mistral-7B and SDXL. In particular, for the LLM fine tuning we implemented a dataprep pipeline that turns websites/pdfs/doc files into question-answer pairs for training the small LLM using an big LLM. It includes a GPU scheduler that can do finegrained GPU memory scheduling (Kubernetes can only do whole-GPU, we do it per-GB of GPU memory to pack both inference and fine tuning jobs into the same fleet) to fit model instances into GPU memory to optimally trade off user facing latency with GPU memory utilization It's a pretty…
2023 · docs.helix.ml · its alternatives →
19
Distributed Llama – Run LLMs on multiple devices in parallel▲12
Distributed LLM inference. Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference. - b4rtaz/distributed-llama
2024 · github.com · its alternatives →
20
Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU▲10
I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions. Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
Jun 2026 · apeg.dev · its alternatives →
21
Run 500B+ Parameter LLMs Locally on a Mac Mini▲17
Hi HN, I built OpenGraviton, an open-source AI inference engine that pushes the limits of running extremely large LLMs on consumer hardware. By combining 1.58-bit ternary quantization, dynamic sparsity with Top-K pruning and MoE routing, and mmap-based layer streaming, OpenGraviton can run models far larger than your system RAM—even on a Mac Mini. Early benchmarks: TinyLlama-1.1B drops from ~2GB (FP16) to ~0.24GB with ternary quantization. At 140B scale, models that normally require ~280GB fit within ~35GB packed. Optimized for Apple Silicon with Metal + C++ tensor unpacking, plus…
Mar 2026 · github.com · its alternatives →
22
Peer-To-Peer Federated Deep Learning Using IPFS and PyTorch▲15
2018 · youtube.com · its alternatives →
23
Serve 100 Large AI models on a single GPU with low impact to TTFT▲7
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 Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com · its alternatives →
24
RunNburn – Run a 295B Moe from a 98GB GGUF on a 64GB RAM Desktop▲11
runNburn is an Apache-2.0 Rust inference engine for quantized GGUF models that are too big for your fast memory. The core idea: weights stay file-backed (mmap), host residency stays under an explicit byte budget (--ram-budget), and GPU caches are sized from detected free/total VRAM — never from device-name presets. There is no conversion step, no sidecar cache files, no silent requantization. The GGUF on disk is the single source of truth. The result that made me want to post this: Tencent's Hy3 (295B total / 21B active sparse MoE, a single 97.8 GiB Q2_K GGUF) runs on my desktop…
Jul 2026 · github.com · its alternatives →

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