DeepSeek-V4 KV Cache Explained: Why 1M Context Uses Less VRAM
DeepSeek-V4 introduces a hybrid compressed attention mechanism that significantly reduces the memory requirements of KV Cache during inference. By compressing multiple historical tokens into fewer KV entries, it alleviates VRAM pressure while maintaining performance for long-context models. This advancement allows for efficient processing of up to 1 million tokens with reduced memory consumption compared to previous architectures.
- ▪DeepSeek-V4's KV Cache is about 10% of DeepSeek-V3.2 and 2% of a common bf16 GQA architecture in a 1M-token setting.
- ▪The model compresses old context into blocks, allowing it to store summaries of earlier tokens instead of full KV for every distant token.
- ▪Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) are two mechanisms used in DeepSeek-V4 to optimize memory usage.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | KnightLi Blog |
| Canonical URL | https://knightli.com/en/2026/05/18/deepseek-v4-kv-cache-compressed-attention/ |
| Publication time | Mon, 25 May 2026 03:10:25 +0000 |
| Retrieval time | 2026-05-25T03:37:35.249Z |
| Last seen | 2026-05-25T03:37:35.249Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | w_qChcMs9x4f |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
AI Industry DeepSeek-V4 KV Cache Explained: Why 1M Context Uses Less VRAM A comparison of DeepSeek-V4's CSA/HCA hybrid compressed attention with traditional MHA, GQA, and MLA, explaining why DeepSeek-V4 can greatly reduce KV Cache memory for 1M-token context. 2026-05-18 8 minute read 中文简体 中文繁體 日本語 Español The real cost of long-context models is often not whether they can accept one million tokens, but how much VRAM the KV Cache consumes during inference. During Transformer decoding, every newly generated token needs access to the Key and Value states of previous tokens. The longer the context, the larger the KV Cache. A larger KV Cache puts pressure on VRAM, memory bandwidth, time to first token, and throughput.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at KnightLi Blog.
Discussion
0 commentsMore from KnightLi Blog
