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AI Engineering
AI Engineering
Apr 24, 2026 · Artificial Intelligence

DeepSeek V4 Unveiled: How Its Million-Token Context Redefines Open-Source LLMs

DeepSeek released the V4 preview, introducing V4‑Pro (1.6 T parameters, 49 B activation neurons, 33 T tokens) and V4‑Flash (284 B parameters, 13 B activation neurons, 32 T tokens) with 1 M token context, a novel DSA sparse attention that reduces compute and memory, and performance that rivals top closed‑source models in agentic coding, world‑knowledge and reasoning benchmarks, while offering an API compatible with OpenAI and Anthropic.

DeepSeekLarge Language ModelMillion Token Context
0 likes · 5 min read
DeepSeek V4 Unveiled: How Its Million-Token Context Redefines Open-Source LLMs
Old Zhang's AI Learning
Old Zhang's AI Learning
Mar 4, 2026 · Artificial Intelligence

Unlock the Full Power of LM Studio for Local LLM Deployment

This article explores LM Studio’s evolution into a complete local AI development platform, detailing version 0.4’s architectural overhaul, headless daemon, parallel request handling, stateful REST API, UI refresh, and a suite of hidden developer features such as OpenAI‑compatible, Anthropic‑compatible APIs, CLI tools, native SDKs, and the LM Link remote‑model solution.

Anthropic APICLILM Link
0 likes · 12 min read
Unlock the Full Power of LM Studio for Local LLM Deployment
Old Zhang's AI Learning
Old Zhang's AI Learning
Jan 27, 2026 · Artificial Intelligence

Qwen3‑Max‑Thinking Boosts Performance with Test‑Time Scaling—Why It Still Isn’t Open‑Source

Alibaba’s new Qwen3‑Max‑Thinking model adds inference‑time scaling and adaptive tool use, delivering large gains on math, coding, and agent benchmarks while remaining closed‑source, and it offers drop‑in OpenAI‑compatible API access at the cost of higher latency and token usage.

AI benchmarkAdaptive Tool UseLarge Language Model
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Qwen3‑Max‑Thinking Boosts Performance with Test‑Time Scaling—Why It Still Isn’t Open‑Source
Ops Community
Ops Community
Jan 18, 2026 · Artificial Intelligence

How to Quadruple LLM Throughput with vLLM’s PagedAttention and Continuous Batching

This guide details how to replace native Transformers inference with the high‑performance vLLM engine, leveraging PagedAttention, continuous batching, tensor parallelism, and OpenAI‑compatible APIs to achieve 3‑4× higher throughput, lower latency, and scalable multi‑GPU deployments for production‑grade large language models.

Continuous batchingGPU OptimizationOpenAI API Compatibility
0 likes · 61 min read
How to Quadruple LLM Throughput with vLLM’s PagedAttention and Continuous Batching