ollm 0.4.2

LLM Inference for Large-Context Offline Workloads

LLM Inference for Large-Context Offline Workloads

oLLM is a lightweight Python library for large-context LLM inference, built on top of Huggingface Transformers and PyTorch. It enables running models like gpt-oss-20B, qwen3-next-80B or Llama-3.1-8B-Instruct on 100k context using ~$200 consumer GPU with 8GB VRAM. No quantization is used—only fp16/bf16 precision.

Latest updates (0.4.2) 🔥

• .safetensors files are now read without `mmap` so they no longer consume RAM through page cache
• qwen3-next-80B DiskCache support added
• qwen3-next-80B (160GB model) added with ⚡️1tok/2s throughput (our fastest model so far)
• Llama3 custom chunked attention replaced with flash-attention2 for stability
• gpt-oss-20B flash-attention-like implementation added to reduce VRAM usage
• gpt-oss-20B chunked MLP added to reduce VRAM usage

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8GB Nvidia 3060 Ti Inference memory usage:

Model	Weights	Context length	KV cache	Baseline VRAM (no offload)	oLLM GPU VRAM	oLLM Disk (SSD)
qwen3-next-80B	160 GB (bf16)	50k	20 GB	~190 GB	~7.5 GB	180 GB
gpt-oss-20B	13 GB (packed bf16)	10k	1.4 GB	~40 GB	~7.3GB	15 GB
llama3-1B-chat	2 GB (fp16)	100k	12.6 GB	~16 GB	~5 GB	15 GB
llama3-3B-chat	7 GB (fp16)	100k	34.1 GB	~42 GB	~5.3 GB	42 GB
llama3-8B-chat	16 GB (fp16)	100k	52.4 GB	~71 GB	~6.6 GB	69 GB

By "Baseline" we mean typical inference without any offloading

How do we achieve this:

• Loading layer weights from SSD directly to GPU one by one
• Offloading KV cache to SSD and loading back directly to GPU, no quantization or PagedAttention
• Offloading layer weights to CPU if needed
• FlashAttention-2 with online softmax. Full attention matrix is never materialized.
• Chunked MLP. Intermediate upper projection layers may get large, so we chunk MLP as well

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Typical use cases include:

• Analyze contracts, regulations, and compliance reports in one pass
• Summarize or extract insights from massive patient histories or medical literature
• Process very large log files or threat reports locally
• Analyze historical chats to extract the most common issues/questions users have

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Supported Nvidia GPUs: Ampere (RTX 30xx, A30, A4000, A10), Ada Lovelace (RTX 40xx, L4), Hopper (H100), and newer

Getting Started

It is recommended to create venv or conda environment first

python3 -m venv ollm_env
source ollm_env/bin/activate

Install oLLM with pip install ollm or from source:

git clone https://github.com/Mega4alik/ollm.git
cd ollm
pip install -e .
pip install kvikio-cu{cuda_version} Ex, kvikio-cu12

💡 Note
qwen3-next requires 4.57.0.dev version of transformers to be installed as pip install git+https://github.com/huggingface/transformers.git

Example

Code snippet sample

from ollm import Inference, file_get_contents, TextStreamer
o = Inference("llama3-1B-chat", device="cuda:0", logging=True) #llama3-1B/3B/8B-chat, gpt-oss-20B, qwen3-next-80B
o.ini_model(models_dir="./models/", force_download=False)
o.offload_layers_to_cpu(layers_num=2) #(optional) offload some layers to CPU for speed boost
past_key_values = o.DiskCache(cache_dir="./kv_cache/") #set None if context is small
text_streamer = TextStreamer(o.tokenizer, skip_prompt=True, skip_special_tokens=False)

messages = [{"role":"system", "content":"You are helpful AI assistant"}, {"role":"user", "content":"List planets"}]
input_ids = o.tokenizer.apply_chat_template(messages, reasoning_effort="minimal", tokenize=True, add_generation_prompt=True, return_tensors="pt").to(o.device)
outputs = o.model.generate(input_ids=input_ids,  past_key_values=past_key_values, max_new_tokens=500, streamer=text_streamer).cpu()
answer = o.tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=False)
print(answer)

or run sample python script as PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True python example.py

Contact us

If there’s a model you’d like to see supported, feel free to reach out at [e-mail]—I’ll do my best to make it happen.