Hoping for Optimized Smarter Upcoming Models .... Like DeepSeek-V4.1-Flash( KVCache + Engram) in Small/Medium/Big sizes

Reddit r/LocalLLaMA 09/13/26, 04:03 PM News

ai-models optimization kvcache engram memory-efficient future-models deep-learning

Summary

The article expresses hope for future AI models with optimizations like KVCache and Engram to reduce memory usage, enabling larger models to run on consumer GPUs with limited VRAM.

It's still a dream for many folks to run medium size(30B range) models @ Q8 with Unquantized KVCache (256K Context) on their GPUs. It would be awesome to have DeepSeek-V4.1-Flash's KVCache + Engram for all Upcoming models. Even for big models. Engram - Heard that approximately 1/3-1/2 of Model size. Might come in different size range too. So 10-15 GB for 30B models. Here few models with approximate numbers. Current models in Odd rows & Future/Fictional models in even rows(Bold). I just put 1GB for 256K context below though DeepSeek-V4.1-Flash takes only same 1GB for 1 million context. Model Model Size 256K KVCache F16 MTP Vision Total GB Qwen3.8-27B-Q8 29 16 1 1 47 Qwen4.0-27B-Q8 29 1 1 1 32 Qwen3.8-27B-Q4_K_M 17 16 1 1 35 Qwen4.0-27B-Q4_K_M 17 1 1 1 20 Muse-Glimmer-30B-Q8 30 16 1 1 48 Muse-Glimmer-2-30B-Q8 30 1 1 1 33 Gemma-4-31B 33 16 1 1 51 Gemma-5-31B 33 1 1 1 36 Qwen3.6-35B-A3B-Q4_K_M 23 6 1 1 31 Qwen4.0-35B-A3B-Q4_K_M 23 1 1 1 26 Gemma-4-26B-A4B-Q8 27 6 1 1 35 Gemma-5-26B-A4B-Q8 27 1 1 1 30 Possibly there might be few more things(Please share those) to keep these number down. So I think 32GB VRAM is more than good enough for Upcoming (Optimized Smarter) Models. RAM is enough for Engram. By above logic(based on Qwen4.0-27B), people could run Q4 of 54B models with same 32GB VRAM. Maybe next year onwards, inventions could make 24GB enough for similar size models.

Original Article

Similar Articles

FlashMemory DeepSeek-V4 Retriever (GitHub Repo)

TLDR AI

Introduces FlashMemory DeepSeek-V4 Retriever, a lightweight model that sparsifies DeepSeek-V4's CSA KV-cache by predicting which chunks will be attended to next, keeping only ~10-15% on-device while matching full-attention performance.

@Michaelzsguo: KV cache is the model’s working memory during generation. As the context window gets longer, the model has to keep more…

X AI KOLs Timeline

DeepSeek's KV cache compression innovations, including MLA and CSA/HCA, reduce KV cache size by 93%, enabling efficient long-context inference and SSD-based caching, as demonstrated by antirez's ds4.c project.

FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention

Hugging Face Daily Papers

Proposes Lookahead Sparse Attention with a Neural Memory Indexer on DeepSeek-V4, reducing GPU memory usage to ~13.5% of full-context baseline while maintaining or slightly improving accuracy.

@0xSero: Best models for your hardware - 4gb to 12gb vram - VibeThinker-3B - smokes everything remotely close to its weight clas…

X AI KOLs Timeline

This thread recommends AI models optimized for different VRAM levels, highlighting VibeThinker-3B for its strong reasoning performance at 3B parameters, along with other models for coding and general use.

@karminski3: Magic! DeepSeekV4 context memory compressed to 1/10! Everyone knows DeepSeekV4 supports 1M context and is heavily optimized. To actually use 1M context, VRAM usage is only about 10GB (compared to DeepSeek-V3.2 which needs about…

X AI KOLs Following

FlashMemory-DeepSeek-V4 proposes a novel inference paradigm called Lookahead Sparse Attention (LSA), which uses a neural memory indexer to actively predict future context needs, compressing physical KV cache usage to 13.5% of full context baseline while improving average accuracy by 0.6%. This method adopts a decoupled training strategy that allows independent training of the indexer without loading the base model, significantly reducing training cost.