AITOPAI热报
ModelsSelected

DeepSeek V4.1-Flash KV cache update

官方账号深度求索 DeepSeek@deepseek_ai
Post
View the post
Why we picked it

This update directly addresses a key cost factor for developers using AI agents, making the model more economical for deployment.

DeepSeek AI released V4.1-Flash, a version of its AI model that reduces the KV cache's memory requirements. The new version needs just 1/4 the HBM and 1/8 the SSD storage compared to the previous generation. This compression cuts costs for agent operations, which often account for a large share of expenses.

Original · 深度求索 DeepSeek

💾 Smaller KV cache. Bigger savings. Compared with the previous generation, V4.1-Flash’s KV cache needs just: 🔹 1/4 the HBM 🔹 1/8 the SSD storage Cache-hit charges often account for a large share of agent costs. Compressing the cache cuts those costs significantly. 3/6 💬 11 🔄 61 ❤️ 1192 👀 96316 📊 127 ⚡

#DeepSeek#AI model#KV cache#cost reduction#DeepSeek AI

Keep reading

Recent stories with the same tag or section
ProductsUS vs ChinaSelected07:26
vLLM sets best DeepSeek-V4.1-Flash result on SemiAnalysis AgentX

Matching DeepInfer's performance while beating it on a third-party agentic benchmark suggests vLLM is now a credible production backend for high-throughput DeepSeek serving, not just a research engine. Engineers running agent-heavy inference stacks can use the reproducible blog recipe to benchmark their own deployments against the 954K tokens/s figure. DeepSeek's off-peak pricing makes the revenue-per-GPU-hour number a useful cost baseline for capacity planning.

#vLLM#DeepSeek#AgentX#SemiAnalysis
官方账号vLLM@vllm_projectSource
ModelsBuildersSelected02:02
vLLM Matches DeepInfer on DeepSeek-V4.1-Flash Decode

An open-source inference stack catching up with a frontier lab's in-house engine means teams can serve DeepSeek-V4.1-Flash without relying on the vendor's own tooling. Engineers running self-hosted deployments in Singapore or ASEAN data centres can benchmark vLLM at DEP32 as a direct alternative to DeepInfer's EP64DP64 setup, avoiding engine lock-in while keeping comparable throughput at large batch sizes.

#vLLM#DeepSeek#DeepInfer#inference
官方账号vLLM@vllm_projectSource