Company

DeepSeek

Chinese artificial intelligence company. Ultrathink has published 13 stories on DeepSeek. Latest, on : DeepSeek v4 Price Change Redefines the Enterprise API Math Against OpenAI and Anthropic.

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Thumbnail for: DeepSeek v4 Pricing: The Cheap AI Era Ends
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DeepSeek v4 Price Change Redefines the Enterprise API Math Against OpenAI and Anthropic

The new DeepSeek v4 pricing adjustment challenges the ultra-cheap cost-performance ratio that made the Chinese AI startup famous. Here is what it means.

Thumbnail for: DeepSeek Harness: Open Sourcing the Evaluation Infrastructure
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How the new DeepSeek Harness democratizes enterprise-grade LLM evaluation and training replication

DeepSeek Harness open-sources the evaluation infrastructure behind the world's most efficient AI models, empowering developers to replicate LLM testing.

Thumbnail for: Alibaba Qwen 3 MoE Leak Reveals Massive 2.4T Model
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Alibaba leaks Qwen 3 MoE model on Hugging Face with 2.4 trillion parameters

Alibaba leaks Qwen3.8-2.4T-A95B-FP8 on Hugging Face, signaling a massive 2.4 trillion parameter MoE model to rival Meta's Llama open-source dominance.

Thumbnail for: DeepSeek V4 Pro Silently Debuts on OpenRouter
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DeepSeek V4 Pro Appears on OpenRouter: What to Expect from the Next-Gen Model

DeepSeek V4 Pro has silently appeared on OpenRouter. Read our analysis of DeepSeek's next-gen model and what it means for API pricing.

Thumbnail for: Cursor Releases Mixture-of-Kittens MoE Megakernel for NVL72
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Why Cursor's Mixture-of-Kittens MoE Megakernel Solves Blackwell's Hidden CPU Bottleneck

The new Mixture-of-Kittens MoE megakernel by Cursor bypasses Blackwell Grace CPU bottlenecks, boosting MXFP8 throughput by 2.37x on NVL72 hardware.

Thumbnail for: Llama.cpp DeepSeek-V4 Support Brings Local Multi-Token Prediction
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How Llama.cpp DeepSeek-V4 Support Delivers Massive Local Inference Performance Gains

Get native llama.cpp DeepSeek-V4 support with release b10228. Run DeepSeek-V4 with Multi-Token Prediction (MTP) locally on consumer hardware today.

Thumbnail for: DeepSeek V4 Flash Redefines Cheap AI Inference
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DeepSeek V4 Flash: Benchmark Data Reveals Next-Gen Price-to-Performance King

DeepSeek V4 Flash benchmark data on Artificial Analysis reveals a new leader in low-cost, high-speed AI inference, pressuring OpenAI and Anthropic.

Thumbnail for: Why Startups Are Fighting to Keep Chinese Open-Weight AI
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How US Startups Became Dependent on Chinese AI Models—and Why a Ban Hurts Silicon Valley

US AI startups are lobbying against a ban on Chinese open-weight AI. Restricting models like Qwen or DeepSeek could cripple American tech innovation.

Thumbnail for: Kimi K3 Performance: Why Distillation Fails to Explain It
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How Moonshot AI Built a Frontier Competitor Without Just Copying Anthropic’s Fable

Analyze Kimi K3 performance and why experts say Moonshot AI did not rely on distilling Anthropic's Fable. Explore the true scaling breakthroughs.

Thumbnail for: US Threatens Chinese Open-Source AI Sanctions
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Why the Treasury’s Threat to Sanction Chinese Open-Source AI Will Fail

US threats of Chinese open-source AI sanctions target IP theft, but enforcing blocks on platforms like Hugging Face presents massive technical hurdles.

Thumbnail for: LangChain Core Adds Standardized Reasoning Effort Parameter
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How the LangChain reasoning effort parameter simplifies managing compute costs for reasoning models

The new LangChain reasoning effort parameter in core 1.5.0 standardizes how developers control compute and cost for reasoning LLMs.

Thumbnail for: vLLM Hits 2,200 Tokens/Second Per H200 for DeepSeek
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vLLM's Wide Expert Parallelism Makes DeepSeek Inference 10x More Efficient at Scale

vLLM achieves 2,200 tokens per second per H200 GPU serving DeepSeek using wide expert parallelism. A technical breakthrough for MoE model inference costs.

Thumbnail for: NVIDIA RTX PRO 5000 72GB Brings Blackwell to Desktop AI
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NVIDIA's 72GB Desktop GPU Makes Running Large Language Models Locally Practical

NVIDIA RTX PRO 5000 72GB Blackwell GPU is now available, giving AI developers enough VRAM to run 70B+ parameter models locally. Here's what it means for agentic AI.