Dev tools · alternatives · 2026
24 alternatives to Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput
Contribute to Mega4alik/ollm development by creating an account on GitHub.
This project enables running the Qwen3-Next-80B model on a graphics card with only 8GB of memory, achieving approximately one token per two seconds of processing speed. It is designed for developers and enthusiasts who… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 2 we have checked on their own sites, 2 of them free or with a free tier.
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I built slotstream, a way to run Qwen3.8-Flash-Next 4-bit on a low-memory mac starting from 16GB, a 125B parameter model that would need 100GB+ memory/RAM, thanks to expert-offloading/ssd-streaming. Easy to install/update, and mac-native using MLX and Swift. It ships with auto-mode, which makes a good tradeoff between memory usage and speed. I'll be implementing and porting the MTP module for speculative decoding next Local models really are the future of computing!
Sep 2026 · github.com · its alternatives →
- 2
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 →
- 3
A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context.…
Jul 2026 · github.com · its alternatives →
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How small can a language model be while still doing something useful? I wanted to find out, and had some spare time over the holidays. Z80-μLM is a character-level language model with 2-bit quantized weights ({-2,-1,0,+1}) that runs on a Z80 with 64KB RAM. The entire thing: inference, weights, chat UI, it all fits in a 40KB .COM file that you can run in a CP/M emulator and hopefully even real hardware! It won't write your emails, but it can be trained to play a stripped down version of 20 Questions, and is sometimes able to maintain the illusion of having simple but terse conversations…
Dec 2025 · github.com · its alternatives →
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Qwen3.8-Flash-Nextfree tier▲108The open-weight preview of Qwen4
Aug 2026 · qwen.ai · its alternatives →
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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 →
- 7WM
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 →
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Qwen3.8-Max▲277Qwen3.8-Max + MCP for coding on your local machine, without paying for Qwen Code. Qwen3.8-Max itself runs in the cloud through Qwen Studio — this setup just gives it access to your local files and terminal through MCP. My take: it is slower than Codex and Claude Code, especially in thinking mode, but it does work well. MCP adds some latency too since there’s an extra layer between the model and your machine. In fast mode, it gets noticeably closer. I wouldn’t put it in the same category as Fable or Opus-5. Personally, I’d place it closer to the next Opus 4.7 level in terms of coding ability.…
Aug 2026 · qwen.ai · its alternatives →
- 9
Qwen3.5 Small▲3550.8B-9B native multimodal w/ more intelligence, less compute
Mar 2026 · huggingface.co · its alternatives →
- 10
Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are…
Jul 2026 · github.com · its alternatives →
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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 →
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Swiftlet is a Swift and Metal runtime that runs large Qwen Mixture-of-Experts models locally on Apple devices by streaming expert weights from storage, enabling 35B and 80B models to run with low RAM, including on iPhone. - leonickson1/Swiftlet
Aug 2026 · github.com · its alternatives →
- 13
The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
2024 · github.com · its alternatives →
- 14
Last week was big for open source LLMs. We got: - Qwen 2.5 VL (72b and 32b) - Gemma-3 (27b) - DeepSeek-v3-0324 And a couple weeks ago we got the new mistral-ocr model. We updated our OCR benchmark to include the new models. We evaluated 1,000 documents for JSON extraction accuracy. Major takeaways: - Qwen 2.5 VL (72b and 32b) are by far the most impressive. Both landed right around 75% accuracy (equivalent to GPT-4o’s performance). Qwen 72b was only 0.4% above 32b. Within the margin of error. - Both Qwen models passed mistral-ocr (72.2%), which is specifically trained for OCR. - Gemma-3…
2025 · github.com · its alternatives →
- 15
Netmind Power▲272Hi HN, We just opened public access to Qwen3‑Coder‑480B‑A35B‑Instruct via a REST/SDK API. Why it might be interesting: - Fully open-source (Apache‑2.0): https://github.com/QwenLM/Qwen3-Coder - 480B params (35B active MoE), long context up to 1M tokens with YaRN - Matches Claude Sonnet/GPT‑4o on SWE-Bench Verified (81.6%) - Free tier: 100K tokens/month
2023 · netmind.ai · its alternatives →
- 16
Greetings HN! This is Doruk from Oblivus, and I'm excited to announce the launch of our platform, Oblivus Cloud. After more than a year of beta testing, we're excited to offer you a platform where you can deploy affordable and scalable GPU virtual machines in as little as 30 seconds! https://oblivus.com/cloud - What sets Oblivus Cloud apart? At the start of our journey, we had two primary goals in mind: to democratize High-Performance Computing and make it as straightforward as possible. We understand that maintaining GPU servers through major cloud service providers can be…
2023 · oblivus.com · its alternatives →
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Qwen3.5▲288The 397B native multimodal agent with 17B active params
Feb 2026 · qwen.ai · its alternatives →
- 18SS
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 →
- 19
How to run Llama3.1 405B on a 8GB VRAM
2024 · youtube.com · its alternatives →
- 20
Metal-first MoE inference for Apple Silicon with bounded SSD expert streaming and local OpenAI Chat, Responses, and Anthropic Messages endpoints. - hebrus-labs/hebrus
Jul 2026 · github.com · its alternatives →
- 21
Explore large language models in 512MB of RAM. Contribute to jncraton/languagemodels development by creating an account on GitHub.
2023 · github.com · its alternatives →
- 22
Calculate token/s & GPU memory requirement for any LLM. Supports llama.cpp/ggml/bnb/QLoRA quantization - RahulSChand/gpu_poor
2023 · github.com · its alternatives →
- 23
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…
2024 · github.com · its alternatives →
- 24AT
A 3.16M-parameter INT4 transformer running entirely in the on-chip memory of a Xilinx Kria KV260. Zero DRAM in the token loop, 59,965 tok/s on the fabric, bit-exact. Chat with it live.
Aug 2026 · mikeayles.com · its alternatives →
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- Qwen3.8-Flash-Next alternatives
- Fine-tune an 8B model on a 4 GB laptop GPU alternatives
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 →