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Engram

8 articles · Page 1 of 1
Architect
Architect
Sep 14, 2026 · Artificial Intelligence

DeepSeek V4.1 Flash Architecture: The State Lifecycle Behind 1M Context

This article dissects DeepSeek V4.1 Flash's architecture for 1M-token context, explaining how CED reduces prefill compute, CSA2 shares global KV across layers, hierarchical sparse indexing narrows search, FP4 quantization shrinks storage, SWA uses bounded replay for recovery, and Engram adds a conditional memory path—revealing five cost dimensions of long-context deployment.

Bounded ReplayCEDCSA2
0 likes · 21 min read
DeepSeek V4.1 Flash Architecture: The State Lifecycle Behind 1M Context
Tech Architecture Stories
Tech Architecture Stories
Sep 13, 2026 · Artificial Intelligence

Engram's $98M Bet: Moving Enterprise AI Beyond RAG to Continuous Learning

Engram raises $98M to replace inference-time RAG with offline 'Study Compute,' using parametric memory, structured notes, and raw retrieval to let models continuously learn enterprise context, demonstrated via a synthetic law firm experiment showing 10x lower cost and better search behavior than base models.

Continuous LearningEngramEnterprise AI
0 likes · 18 min read
Engram's $98M Bet: Moving Enterprise AI Beyond RAG to Continuous Learning
Architect
Architect
May 25, 2026 · Artificial Intelligence

From KV Cache to Harness: How DeepSeek Is Shifting Costs to the System Layer

DeepSeek’s recent V4 release shows that as model inference becomes cheaper, the dominant expenses are moving to system‑level components such as KV cache, memory, storage, compilers, scheduling, hardware adapters, and the emerging Agent Harness layer, reshaping AI infrastructure economics.

AI InfrastructureCost OptimizationDeepSeek
0 likes · 23 min read
From KV Cache to Harness: How DeepSeek Is Shifting Costs to the System Layer
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 31, 2026 · Artificial Intelligence

How Engram Lets Large Models Swap GPU Memory for Cheap RAM to ‘Look Up’ Knowledge

The article dissects DeepSeek’s new Engram architecture, which separates computation from memory by using a large, cheap‑RAM‑based lookup table to store factual knowledge, allowing the transformer’s compute layers to focus on reasoning, dramatically reducing GPU memory demand while improving code, math, and long‑context performance.

EngramGPU memoryMemory-Compute Architecture
0 likes · 7 min read
How Engram Lets Large Models Swap GPU Memory for Cheap RAM to ‘Look Up’ Knowledge
DataFunTalk
DataFunTalk
Jan 13, 2026 · Artificial Intelligence

How Conditional Memory (Engram) Boosts Large Language Models Beyond MoE

DeepSeek's new paper introduces a conditional memory mechanism called Engram that complements Mixture‑of‑Experts, providing O(1) lookup, improving knowledge retrieval, reasoning, and long‑context performance while scaling efficiently on the same FLOPs budget.

EngramLarge Language Modelsconditional memory
0 likes · 18 min read
How Conditional Memory (Engram) Boosts Large Language Models Beyond MoE
PaperAgent
PaperAgent
Jan 13, 2026 · Artificial Intelligence

How Engram’s Conditional Memory Redefines Sparsity in Large Language Models

DeepSeek’s newly released Engram module introduces a conditional memory mechanism that leverages O(1) N‑gram lookup to create a new sparsity axis for large language models, reducing early‑layer compute, improving inference efficiency, and delivering notable performance gains across reasoning and knowledge tasks, as demonstrated by extensive experiments on 27‑billion‑parameter models.

EngramLLM SparsityLarge Language Models
0 likes · 8 min read
How Engram’s Conditional Memory Redefines Sparsity in Large Language Models