# You Can Run A 27B AI On A 16GB GPU At 100K Tokens/S \[NInfer\] **URL:** \ **Category:** Content Creators\ **Tags:** the-stack, gpu, nvidia-geforce, machine-learning, hardware, running-locally, quantisation\ **Created:** [6 October 2026 21:16 UTC](https://www.artofsm.art/t/you-can-run-a-27b-ai-on-a-16gb-gpu-at-100k-tokens-s-ninfer/25157 "2026-10-06T21:16:31Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [6 October 2026 21:16 UTC](https://www.artofsm.art/t/you-can-run-a-27b-ai-on-a-16gb-gpu-at-100k-tokens-s-ninfer/25157/1 "2026-10-06T21:16:31Z")
[![](https://www.artofsm.art/uploads/default/original/3X/5/a/5a898a5016d807f8f92c294a3deb219b623d6ba9.jpeg "You Can Run A 27B AI On A 16GB GPU At 100K Tokens/S [NInfer]") ](https://www.youtube.com/watch?v=rBBIIBjK8UU) Developer Roofkid’s Ninfer 4080 engine enables running a 27 billion parameter AI model with a 100,000 token context on a single 16 GB RTX 4080 GPU by compressing model weights to 3 bits and optimizing memory usage, achieving prompt reading speeds up to 2,700 tokens per second and writing speeds around 100-262 tokens per second. While delivering significant performance gains over general-purpose engines like llama.cpp on the same hardware, Ninfer is highly specialized for the RTX 4080, requires a Linux environment, and its output quality and stability remain preliminarily tested. ---
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [6 October 2026 21:37 UTC](https://www.artofsm.art/t/you-can-run-a-27b-ai-on-a-16gb-gpu-at-100k-tokens-s-ninfer/25157/3 "2026-10-06T21:37:15Z")
A developer named Roofkid has created a custom AI engine called Ninfer 4080 that can run a 27 billion parameter model with a massive 100,000 token context on a single 16 GB RTX 4080 graphics card. This is achieved by compressing the model weights down to 3 bits each and reducing the conversation memory size to about 2 GB, allowing the entire setup to fit within the limited GPU memory. The engine reads prompts at speeds up to around 2,700 tokens per second initially, tapering to about 2,000 tokens per second for very long inputs, enabling it to process a full 100,000 token prompt in under a minute. Writing speeds peak at 262 tokens per second on repetitive benchmark text but average closer to 100-200 tokens per second in real-world coding and prose tasks. The compression to 3-bit weights is critical to fitting the model and conversation history into the 16 GB GPU memory, as a full 16-bit precision model would require over 50 GB. Despite concerns that such aggressive compression might degrade output quality, early tests show the 3-bit version performs comparably to full models on benchmarks like the AIM 2025 math competition and coding tasks, scoring around 90%. However, these results come from limited tests by the developer and should be considered preliminary rather than definitive proof of quality. Ninfer’s speed advantage over general-purpose AI engines comes from its narrow focus on a single GPU architecture and model, allowing it to better utilize the GPU’s memory bandwidth. Compared to llama.cpp running the same model on an RTX 4080, Ninfer reads prompts roughly 14 times faster and writes faster as well, though the writing speed comparison is less direct due to differences in testing methods. This specialized tuning sacrifices broad hardware compatibility but delivers significant performance gains on the targeted RTX 4080 card. The engine is currently only compatible with the RTX 4080 due to its highly specialized tuning and compilation for that specific GPU architecture. Other 16 GB GPUs in the Nvidia 40 series may run the engine but with unknown performance and stability, while newer Blackwell generation cards require different forks. Users with AMD, older Nvidia cards, or Apple Silicon Macs are advised to use llama.cpp instead, which supports a wider range of hardware but at lower speeds. Running Ninfer requires a Linux environment with CUDA 13.1+, Docker, and Nvidia’s container toolkit. In summary, Roofkid’s Ninfer 4080 demonstrates that it is possible to run a large 27B parameter AI model with an extremely long 100,000 token context on a single 16 GB RTX 4080 GPU by using aggressive compression and specialized engineering. The engine achieves impressive reading and writing speeds that outperform standard tools on the same hardware, though these results come from a single developer’s tests and have yet to be independently verified. For RTX 4080 owners working with massive documents, Ninfer offers a promising option, while others can use llama.cpp or forks tailored to their hardware. The key takeaway is to carefully consider whether reported AI speeds refer to reading or writing and the nature of the test data used. ## Useful Links - [NInfer-4080 GitHub Repository](https://github.com/roofkid/ninfer-4080) — Directly provides the custom inference engine discussed in the video, enabling replication and use of the described setup. - [Qwen 3.8 27B 3-Bit GSQ Model by ISTA-DASLab on Hugging Face](https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-3Bit-GSQ) — Provides the compressed model weights essential for running the 27B parameter model with 3-bit quantization as described. - [Qwen 3.8 27B NInfer Model by Roofkid on Hugging Face](https://huggingface.co/roofkid/Qwen3.8-27B-GSQ3-NInfer) — Provides the exact model file used by the NInfer engine for inference on RTX 4080. - [llama.cpp GitHub Repository](https://github.com/ggml-org/llama.cpp) — Provides a widely used alternative inference engine for large language models, useful for comparison and fallback on other hardware.