AI · alternatives · 2026
24 alternatives to Tune LLaMa3.1 on Google Cloud TPUs
Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs). The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs. But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla…
This project provides tools and code for fine-tuning and serving Llama 3.1 on Google Cloud TPUs as an alternative to NVIDIA GPUs. It targets machine learning developers and organizations seeking cost-effective… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Tune LLaMa3.1 on Google Cloud TPUs launched in 2024; newer entries below may have overtaken it.
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Hi HN! I'm just sharing a project I've been working on during the LLM Efficiency Challenge - you can now finetune Llama with QLoRA 5x faster than Huggingface's original implementation on your own local GPU. Some highlights: 1. Manual autograd engine - hand derived backprop steps. 2. QLoRA / LoRA 80% faster, 50% less memory. 3. All kernels written in OpenAI's Triton language. 4. 0% loss in accuracy - no approximation methods - all exact. 5. No change of hardware necessary. Supports NVIDIA GPUs since 2018+. CUDA 7.5+. 6. Flash Attention support via Xformers. 7. Supports 4bit and 16bit…
2023 · github.com · its alternatives →
- 2
I've been playing around with https://github.com/zphang/minimal-llama/ and https://github.com/tloen/alpaca-lora/blob/main/finetune.py, and wanted to create a simple UI where you can just paste text, tweak the parameters, and finetune the model quickly using a modern GPU. To prepare the data, simply separate your text with two blank lines. There's an inference tab, so you can test how the tuned model behaves. This is my first foray into the world of LLM finetuning, Python, Torch, Transformers, LoRA, PEFT, and Gradio. Enjoy!
2023 · github.com · its alternatives →
- 3
Hi everyone, I'm kinda involved in some retrogaming and with some experiments I ran into the following question: "It would be possible to run transformer models bypassing the cpu/ram, connecting the gpu to the nvme?" This is the result of that question itself and some weekend vibecoding (it has the linked library repository in the readme as well), it seems to work, even on consumer gpus, it should work better on professional ones tho
Feb 2026 · github.com · its alternatives →
- 4
Llama▲3123.1-405B: an open source model to rival GPT-4o / Claude-3.5
2024 · developer.meta.com · its alternatives →
- 5
I spent the last few days building out a nicer ChatGPT-like interface to use Mistral 7B and Llama 3 fully within a browser (no deps and installs). I’ve used the WebLLM project by MLC AI for a while to interact with LLMs in the browser when handling sensitive data but I found their UI quite lacking for serious use so I built a much better interface around WebLLM. I’ve been using it as a therapist and coach. And it’s wonderful knowing that my personal information never leaves my local computer. Should work on Desktop with Chrome or Edge. Other browsers are adding WebGPU support as well - see…
2024 · github.com · its alternatives →
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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 →
- 7
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 →
- 8
Accelerating open machine learning research with Cloud TPUs
2017 · sites.research.google · its alternatives →
- 9WM
Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…
2024 · glhf.chat · its alternatives →
- 10
SelfHostLLM▲134A simple calculator that estimates how many concurrent requests your GPU can handle for a given LLM, with shareable results.
2025 · selfhostllm.org · its alternatives →
- 11
Fine-tune LLM agents with online reinforcement learning - KhoomeiK/LlamaGym
2024 · github.com · its alternatives →
- 12
Build and train machine learning models with Google
2017 · blog.google · its alternatives →
- 13
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Arkor▲142Fine-tune and Deploy Open-weight Models in TypeScript
Jul 2026 · arkor.ai · its alternatives →
- 15
Calculate token/s & GPU memory requirement for any LLM. Supports llama.cpp/ggml/bnb/QLoRA quantization - RahulSChand/gpu_poor
2023 · github.com · its alternatives →
- 16TC
Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…
2022 · tensordock.com · its alternatives →
- 17SS
Running DeepSeek V3 (685B) requires 8×H100 GPUs which is about $14k/month. Most developers only need 15-25 tok/s. sllm lets you join a cohort of developers sharing a dedicated node. You reserve a spot with your card, and nobody is charged until the cohort fills. Prices start at $5/mo for smaller models. The LLMs are completely private (we don't log any traffic). The API is OpenAI-compatible (we run vLLM), so you just swap the base URL. Currently offering a few models.
Apr 2026 · sllm.cloud · its alternatives →
- 18
Hi HN! I built LLMKube, a Kubernetes operator for deploying GPU-accelerated LLMs in production. One command gets you from zero to inference with full observability. Why this exists: Regulated industries (healthcare, defense, finance) need air-gapped LLM deployments, but existing tools are either single-node only (Ollama) or lack GPU optimization and SLO enforcement. LLMKube bridges the gap. What's working: - 17x speedup with NVIDIA GPUs (64 tok/s on Llama 3.2 3B vs 4.6 tok/s CPU) - One command: llmkube deploy llama-3b --gpu (auto CUDA setup, scheduling, layer offloading) -…
Nov 2025 · github.com · its alternatives →
- 19
TorchTPU▲106Running PyTorch Natively on TPUs at Google Scale
Apr 2026 · cloud.google.com · its alternatives →
- 20
Looking for the cheapest place to deploy llama 3.1 model? Don't worry we have found it so you don't have to.
2024 · github.com · its alternatives →
- 21AT
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
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Transform generic AI models into specialized solutions
2025 · tokenfactory.nebius.com · its alternatives →
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Check out this impressive project that enables running LLMs entirely in the browser using WebGPU. Key features: - Zero token costs, no cloud infrastructure required - Complete data privacy through local processing - Simple 3-line code integration - Built on MLC and Transformer.js The benchmarks show smaller models can effectively handle many common tasks. Currently the project roadmap includes: - No-code AI pipeline builder - Browser-based RAG for document chat - Analytics/logging - Model fine-tuning interface
2025 · github.com · its alternatives →
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2024 · colab.research.google.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →