KIV: 1M Tokens on 12GB VRAM No Retrain

💡1M context LLMs on 12GB GPUs, no retrain—game-changer for local inference

⚡ 30-Second TL;DR

What Changed

1M tokens on RTX 4070 12GB VRAM

Why It Matters

Enables long-context inference on consumer hardware, democratizing large LLM use for practitioners.

What To Do Next

pip install git+https://github.com/Babyhamsta/KIV and test on Gemma-4 E2B.

Who should care:Developers & AI Engineers

AI-generated analysis for this event.

•KIV utilizes a vector-quantized K-index to perform approximate nearest neighbor search, allowing the system to selectively load only the most relevant KV pairs from system RAM into VRAM during the attention computation.
•The implementation leverages a custom CUDA kernel for the retrieval process, which minimizes the latency overhead typically associated with CPU-to-GPU data transfers in tiered memory architectures.
•Unlike standard sliding window attention or sparse attention mechanisms, KIV maintains a global context window, ensuring that the model retains access to the entire 1M token history rather than discarding older information.

📊 Competitor Analysis▸ Show

Feature	KIV	vLLM (PagedAttention)	FlashAttention-3
Memory Strategy	Tiered (VRAM/RAM)	Paged VRAM	Optimized VRAM kernels
Max Context	1M+ (Hardware limited)	VRAM capacity limited	VRAM capacity limited
Retraining	None	None	None
Primary Use Case	Consumer GPU (12GB)	High-throughput serving	Training/Inference speed

K-Index Structure: Employs a hierarchical clustering approach to index Key vectors, enabling sub-linear time complexity for retrieval during the attention phase.
Cache Management: Implements a 'hot-cold' cache policy where the most recent N tokens are pinned in VRAM, while the remaining M-N tokens are stored in a compressed format in system RAM.
Integration: Designed as a drop-in replacement for HuggingFace's DynamicCache class, allowing it to hook into existing transformers pipelines without modifying model weights.
Quantization: Supports optional 4-bit or 8-bit quantization of the cached Key vectors to further reduce the RAM footprint for extremely long sequences.

Consumer-grade hardware will become the standard for long-context RAG applications.

By decoupling context length from VRAM capacity, KIV removes the primary hardware barrier for running massive document analysis on affordable GPUs.

Memory-tiering will replace pure VRAM caching in mainstream inference engines.

The performance trade-off of PCIe bandwidth is increasingly outweighed by the utility of near-infinite context windows in local LLM deployments.

2026-02

Initial release of KIV repository on GitHub by Babyhamsta.

2026-03

Integration support added for Phi-3.5 and Qwen2.5 architectures.

2026-04

Public demonstration of 1M token context on RTX 4070 hardware.

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Original source: Reddit r/MachineLearning ↗