oLLM: Bringing Ultra-Long Context LLM Inference to Consumer GPUs

oLLM is an innovative Python library which enables researchers, hobbyists, and developers to execute Generative AI models with large context windows – up totokens – on standard NVIDIA GPUs with as little as 8 GB VRAM, by using smart SSD offloading methods, and with none of the headaches of quantization. This post disaggregates what is unique about oLLM, the models and hardware it can work with, and how it transforms the game of single-GPU inference.

What Sets oLLM Apart?

Historically, operations on large transformer models with large contexts were only possible by either costly or very pricey, multi-GPU servers, or by trading off precision by heavily quantizing models and attention caches, allowing them to run on consumer-friendly local SSDs, including even RTX 3060 Ti systems. oLLM reverses this trend by streaming model weights and attention cache to fast local SSDs, squeezing more context into consumer-friendly hardware, even with RTX 3060 Ti.

Key innovations:

• Streams loads on-demand of SSD directly into the GPU memory.
• Offloads attention KV-cache to SSD, retaining VRAM low allowing token contexts much larger than normal hardware limits.¹
• Combines FlashAttention-2 and home-crafted disk-backed caching plans, reducing the RAM consumption and allowing users to work with enormous documents or logs offline.²

Supported Hardware and Models

oLLM is compatible out-of-the-box with popular architectures, including:

• Llama-3 (1B, 3B, 8B)
• GPT-OSS-20B
• Qwen3-Next-80B (a sparse MoE, with 80B parameters, only 3B of which are ever active at once)⁴

It is NVIDIA Ampere (RTX 30xx, A-series), Ada (RTX 40xx, L4), and Hopper-based. NVMe SSDs with a high speed are highly suggested as storage bandwidth and latency are the primary source of performance limitations.

How Does It Work?

Imagine oLLM as a LLM weight and cache streamer:

• Parameters of a model reside on disk and are only sent into a GPU on-demand.
• Attention KV-caches are transferred to SSD and level the usage of VRAM even with increased context sizes.⁵
• Calls FlashAttention-2, online softmax, and chunked MLP projections– that is, no huge intermediate attention matrices are formed and memory peaks are managed.⁶

This clever storage-based solution sacrifices flexibility to throughput. Users are now able to perform inference on a model that was previously only available to multi-gpu clusters, but only with high-speed local storage available (such as 100-200 GB in the case of Qwen3-Next-80B at 50K tokens).

Performance: Trade-Offs and Results in the Real World

Although oLLM opens up the possibilities of what can be done in consumer hardware dramatically, it is important to place expectations correctly:

• Throughput: With the mammoth Qwen3-Next-80B in the 50K context, users can expect approximately 0.5 tokens per second on a RTX 3060 Ti– it is not brisk chatbot, but suitable when analysing documents.
• Storage needs: Huge contexts scale directly to very large disk writes, making SSD speed (and capacity) the new bottleneck, and not VRAM.
• Scalability: It is practical to run such large models to drive offline analytics or compliance or batch summarization, but oLLM is not a drop-in substitute to high-speed serving systems such as vLLM or TGI.

Installation

Installing the software is not difficult. The project is an open-source, under MIT license, and can be found on PyPI as:

pip install ollm

The optional kvikio-cu package supports high-speed disk I/O, and the most recent versions of Qwen3-Next require a dev build of Huggingface Transformers.⁷ The README illustrates how to configure disk caching and perform streaming inference using simple Python calls.

Final Thoughts

oLLM is unique in its ability to allow users to maintain the full-precision inference but expand context windows to the tens of thousands- on normal GPUs. It is not about beating enterprise-grade throughput, but rather of making massive context LLM accessible to offline tasks: document review, summarization, and compliance checks.⁸

Well, have you ever wanted to do something your hardware could not, or felt let down by the loss in accuracy caused by quantization, then oLLM reinvents what is possible on a single workstation. As systems and context demands of large models grow swiftly, new opportunities are available to everyone pursuing the next milestone of generative AI, and it is all within the comfort of a local setup with tools such as oLLM available to everyone.Well, have you ever wanted to do something your hardware could not, or felt let down by the loss in accuracy caused by quantization, then oLLM reinvents what is possible on a single workstation. As systems and context demands of large models grow swiftly, new opportunities are available to everyone pursuing the next milestone of generative AI, and it is all within the comfort of a local setup with tools such as oLLM available to everyone[1].