AI article
ISOM-R2: Streaming 1,055,402 Tokens on 3.24 GB Peak VRAM
Community description: Standard Transformer attention has a memory problem at scale. For a 1.05 million-token context with...
Dev.to | Oct 2, 2026 | Prannessh KVA
Automated excerpt
Instead of maintaining a linearly growing KV cache across every token, ISOM-R2 processes context through bounded isometric state projections, keeping GPU memory flat regardless of sequence length. Prefill VRAM Flat at 2. 97 GB across all 1M tokens Memory vs. No token ever materializes a full KV cache entry in GPU memory.
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