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The article criticizes Microsoft AI's lackluster coding model performance compared to rivals like Kimi K3 and Deepseek V4, suggesting MAI is far behind despite vast resources.
DeepSeek V4 Flash (98GB) now runs up to 7 tokens per second on a single RTX 4060 Ti with CPU offloading, a 3x speed improvement over the previous week's 2 t/s.
A user shares their experience running the DeepSeek v4 Flash model with a 24GB GPU and DDR5 RAM, including performance numbers and tips for optimization.
Merged fixes for quantized KV cache into the DeepSeek V4 branch of llama.cpp, with benchmark perplexity results for f16, q8_0, and q4_0 cache types.
SGLang provided Day-0 support for DeepSeek-V4, and collaboration between LMSys and NVIDIA engineering teams achieved up to 5x throughput increase in production, with improvements shown on the SemiAnalysis InferenceX dashboard.
DwarfStar is a self-contained native inference engine optimized for DeepSeek V4 Flash and PRO models, supporting Metal, CUDA, and ROCm backends, with a focus on high-end personal machines and Mac Studios.
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.
A developer ran DeepSeek-V4-Flash on a Raspberry Pi 5 by streaming model weights from an NVMe SSD, achieving 1.3 tokens/second at 8 watts, demonstrating the feasibility of frontier-adjacent open-weight models on low-cost, offline hardware.
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.
According to the DeepSeek V4 technical report's evaluation of mainstream LLMs, Gemini 3.1 Pro is considered to have the strongest world knowledge, but users generally find it hard to use because the model does not proactively use search tools.
A personal test claims DeepSeek v4 surpasses Opus 4.8 in Chinese writing, asserting that DeepSeek v4 is currently the strongest model for Chinese writing.
DeepSeek V4 Flash is now available again for free on Nous Portal for use in Hermes Agent.
A developer successfully runs DeepSeek-V4-Flash (284B total, 13B active) locally on four RTX 2080 Ti GPUs with a $2,500 budget, achieving 255 prefill tokens/s using custom Turing CUDA kernels, W8A8 quantization, and heterogeneous inference. The implementation is open-sourced.
A detailed evaluation of Deepseek V4's 1M token context window across production codebases reveals optimal performance at 150-250k tokens, with degradation past 300k and significant latency in reasoning mode. The model exhibits high hallucination rates on unknown tasks, requiring validation layers for production use.
A guide on using DeepSeek V4 as a cheaper alternative to Claude Opus 4.7 for agentic coding in Claude Code, including setup steps and cost comparison.
A developer used Codex 5.5 as an orchestrator and Deepseek v4 pro as an executor to generate a 240M token fine-tuning dataset, burning 359M tokens at a cost of only $78.
DeepSeek released V4 Pro and V4 Flash under MIT license on April 24, 2026. In benchmarks against Claude Opus 4.7 and Kimi K2.6, V4 Pro scored 77/100 at $2.25, placing between Opus 4.7 (91) and Kimi K2.6 (68), while V4 Flash scored 60/100 at $0.02, the cheapest in the comparison, with a 75% discount on V4 Pro through May 31.
Anthropic unveils a withheld Claude Mythos model that autonomously finds thousands of 0-days, ZAI open-sources the 1.5 TB GLM-5.1 that tops open-weight benchmarks, Alibaba’s unreleased HappyHorse video model hits #1 on public leaderboards, and Deepseek teases an “Expert Mode” v4 preview.