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DeepSeek V4 demands 70 GB KV cache for 1M token context

By PulseAugur Editorial · [1 sources] ·

DeepSeek's latest model, DeepSeek V4, requires a substantial 70 GB of KV cache to handle a 1 million token context window. While the specific configuration for V4 remains private, details from the V3 model offer insight into the significant GPU memory demands associated with such large context lengths. AI

IMPACT Highlights the significant infrastructure costs and memory requirements for large context windows in frontier models.

RANK_REASON Frontier-lab model release with system card [lever_c_demoted from frontier_release: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepSeek V4 demands 70 GB KV cache for 1M token context

How we ranked this

Signal score
3 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Significant
Frontier-lab model release with system card [lever_c_demoted from frontier_release: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, infra
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

Full methodology in our editorial standards.

COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · pickuma ·

    DeepSeek MLA: 70 GB of KV Cache at 1M Tokens No DeepSeek-V4 config is public yet. The V3 one is, and its KV-cache math tells you what a million-token window act

    DeepSeek MLA: 70 GB of KV Cache at 1M Tokens No DeepSeek-V4 config is public yet. The V3 one is, and its KV-cache math tells you what a million-token window actually costs in GPU memory. https:// pickuma.com/for-dev/deepseek-m la-kv-cache-million-token-context/?utm_source=mastodo…

    LINKS pickuma.com/…/deepseek-mla-kv-cache-milli…

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