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I gave a 21M model a 6.4B-parameter lookup table. It matches a 114M dense model and runs with the table on an SSD (RX 9070)

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 15:47:25 UTC.

Briefings

A 21M parameter model with a 16.8M-row sparse lookup table (6.4B parameters) matches a 114M dense model on Wikipedia tokens. The table runs on an NVMe SSD with 0.4 GB VRAM, achieving 140 tok/s on an RX 9070. The study was conducted by /u/fechyyy with Claude Code, using 500M Wikipedia tokens. Performance degrades by 4 KB per missed row. Results are preprint with no commercial claims.

Key points

• A 21M parameter model with a 16.8M-row sparse lookup table (6.4B parameters) matches a 114M dense model on 500M Wikipedia tokens.
• The sparse table runs on an NVMe SSD with 0.4 GB VRAM, achieving 140 tokens per second on an RX 9070.

Context

The study investigates sparse memory for LLMs, referencing prior work on product-key memory and Meta's Memory Layers at Scale. It was conducted by /u/fechyyy using 500M Wikipedia tokens, with results preprint status and no commercial claims.

Limitations

Results are limited to a single seed run on a gaming PC; performance degrades by 4 KB per missed row; text is fluent Wikipedia English with made-up facts; no larger scale testing or commercial validation provided.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 29/10/2026, 23:29:48 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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