vLLM and SGLang matched or exceeded DeepSeek's custom DeepInfer engine on DeepSeek-V4.1-Flash within days by adopting its open-sourced operators and optimizing for model-specific traits, proving general-purpose inference engines remain dominant due to full production-grade systems.
Analysis shows HiRoute's intelligent model routing saves over 90% costs despite cache loss when switching models, because switches are infrequent, one-time losses amortize over many requests, cheaper models often cost less even with full reprocessing, and cache discounts only affect input while savings come from output-heavy cheaper models.
Alibaba Cloud expert Wu Guanying at the 2026 Advanced Storage Conference presents HBF as a high-bandwidth flash memory offering 8-16x HBM capacity at 1/10 $/GB, suitable for KV cache and model weights in AI inference, but faces challenges in latency, write bandwidth, software stack, and ecosystem maturity, requiring cross-industry collaboration for 2027 sampling and 2028 mass production.
DeepSeek releases DeepInfer inference framework and deepseek-recipe tokenizer under Apache 2.0, disclosing 155 trillion tokens/day throughput, $38/GPU/hour revenue at list price, and an 82% gross margin assuming $7/GPU/hour rental, with technical details on KV cache retention, speculative decoding, and deployment optimizations.
This comprehensive guide explains inference frameworks for large language models, covering core concepts like Prefill/Decode and KV Cache, comparing major open-source frameworks (vLLM, SGLang, TensorRT-LLM), detailing request processing flow from scheduling to sampling, and identifying who benefits from understanding inference infrastructure.
At OCP 2026 Alibaba Cloud detailed a shift from GPU-centric to CPU-GPU co-evolution for Agentic AI, unveiling the UMX architecture with CXL memory pooling, UALink interconnect, and multi-tier persistent memory (AliSCM) that delivered 58% throughput gains in KV Cache-heavy inference tests.
This comprehensive guide details a five-step methodology for assessing GPU, VRAM, storage, and network requirements when deploying large language models privately, covering weight and KV cache calculations, hardware selection trade-offs, and validation commands.
This article explains why AI models lose accuracy in long conversations due to context degradation, detailing six failure mechanisms and five practical strategies—layered context organization, active compaction, retrieval over accumulation, state externalization, and selective retention—to maintain reliable, cost-effective LLM interactions for operations tasks.
HiRoute is a local intelligent routing engine that dynamically assigns tasks between economical and powerful AI models, enables Agent collaboration, preserves KV cache via context holding, and integrates decision models like Jev, demonstrating over 90% cost reduction in real-world engineering tasks while maintaining quality.
This guide analyzes Mac Studio M5 memory options (128GB vs 256GB) for running local large language models, detailing model size limits, quantization impact, KV cache overhead, memory bandwidth differences, and a ¥35K price gap to help developers choose the right tier for their workload.
This article explains why CPUs struggle with MoE model prefill but excel at decode, compares dense, SSM, MLA, and BitNet architectures, evaluates CPU, NVIDIA GPU, Apple UMA, and NPU hardware for local LLM inference, and provides a practical tuning guide for running 32B models on a 32GB Mac mini with MLX and Ollama.
The article analyzes six systemic flaws in current coding agents caused by KV cache dependency, proposing a Harness framework centered on the Jev decision model that dynamically assembles context, routes models with full cost accounting, progressively discloses tools, binds instructions to context triggers, and shares read-only retrieval across background pipelines.
A Tencent senior backend engineer with 15 years experience shares his transition to LLM inference engineering, detailing learning paths for model architecture, inference optimization, and self-assessment exercises, while arguing that traditional infrastructure skills remain crucial for AI Infra and Agent Infra development.
Salesforce AI Research and UIUC propose Random Attention, a simple KV cache eviction method that randomly retains reasoning tokens while protecting the prompt, matching state-of-the-art baselines across math, science, and code reasoning tasks and improving inference throughput by 32–43% in vLLM serving.
Sugon's FN Neo redefines centralized all-flash storage for AI inference by turning KV Cache into a shared cluster-level asset, delivering native KV semantics with sub-millisecond latency, super-tunnel contention-free data paths, and three-tier load balancing that yields 6–12× context-length speedups on DeepSeek-R1 and Qwen-2.5 across single-node, multi-node, and real-world AI coding workloads.
The article analyzes Apple M5 Ultra 256GB unified memory capacity for local LLM inference, showing Q4-quantized models from 27B to 200B+ fit, but KV cache overhead for long contexts reduces headroom; it compares with multi-GPU setups and advises on purchase decisions.
This article dissects DeepSeek V4.1 Flash's architecture for 1M-token context, explaining how CED reduces prefill compute, CSA2 shares global KV across layers, hierarchical sparse indexing narrows search, FP4 quantization shrinks storage, SWA uses bounded replay for recovery, and Engram adds a conditional memory path—revealing five cost dimensions of long-context deployment.
Sugon's scaleFabric achieves China's first large-scale IBGDA deployment, letting GPUs directly drive InfiniBand NICs without CPU involvement; validated in 100K-GPU clusters, it eliminates the CPU penalty for small-packet MoE All-to-All communication, cuts latency by two orders of magnitude, and integrates NVMe-over-RDMA and XPU Direct Storage for end-to-end Token acceleration.
This article traces the Loop architecture lineage from Universal Transformer to recurrent depth, examines engineering challenges like placement, state drift, dynamic stopping, and KV cache, and argues that as internal computation decouples from parameters and visible reasoning, Agent verification must rely on external evidence like tests, approvals, and versioned artifacts rather than chain-of-thought alone.
Microsoft research shows sliding window attention with four attention sinks achieves 99% performance recovery on knowledge tasks without any post-training, matching linear attention methods requiring hundreds of millions of tokens while outperforming them 2-10x on long-context benchmarks.
This article explains why GPUs underutilize during LLM inference and compares static, dynamic, and continuous batching strategies, detailing their trade-offs, the role of KV cache, iteration-level scheduling, and chunked prefill to optimize throughput for variable-length outputs.
This article analyzes how DeepSeek Harness achieves 99% prompt cache hit rates by enforcing deterministic request assembly, showing that cache hit rates serve as a diagnostic tool for runtime stability rather than just a cost metric.
Prefix Sliding keeps the task prefix and a sliding window of recent tokens while evicting older intermediate tokens from the KV cache, enabling up to three‑fold speedups for long‑chain inference without retraining and extending reinforcement‑learning rollouts beyond 100 k tokens.
Round‑robin routing in Kubernetes wipes out KV‑cache benefits for LLM inference, but llm‑d introduces cache‑aware routing, hierarchical eviction, and prefill/decode separation, delivering up to three‑fold throughput gains and halving first‑token latency, as shown in Tesla's production rollout.
OpenAI’s first Jalapeño results show a custom inference chip that prioritizes per‑watt AI work and latency over raw speed, detailing system‑level design, AI‑assisted development, and benchmark gains across multiple large models while outlining the limits of the current data.
The article analyzes why the same LLM can exhibit vastly different cost, speed, and concurrency across inference systems, showing that KV‑cache memory management, continuous batching, PagedAttention, quantization trade‑offs, and speculative decoding together determine real‑world throughput and latency.
This article walks through the core concepts needed before deploying a large language model on‑premises, covering weight precision, quantization methods, model packaging formats, inference engines, GPU memory considerations, KV‑cache sizing, sampling strategies, optional extensions such as LoRA and RAG, and a step‑by‑step decision workflow to match hardware, model, and deployment goals.
DeepSeek raised its cache‑hit price up to 11 times as exploding long‑context demand forces a shift to tiered KV storage, exposing hidden storage, I/O and scheduling costs that turn GPU compute into costly data‑movement, prompting developers to rethink cache strategies.
The openJiuwen platform introduces a semantic coordination layer called Agent Hint, together with SAM and SPM managers, to align agent task states with compute resources, achieving a 57% reduction in first‑token latency, a 27.6% drop in end‑to‑end latency, a 33% increase in cache hit rate, and a 25% decrease in storage peak for multi‑agent inference workloads.
The article reviews Coleman Hooper's 2026 Berkeley PhD thesis, which shows that LLM inference is increasingly limited by memory bandwidth and capacity, and proposes a four‑pronged approach—weight quantization, KV‑cache quantization, selective context loading, and multipole attention—to dramatically improve memory efficiency and throughput.
The paper proposes Agent Primitives—reusable latent building blocks for multi‑agent systems—that replace hand‑crafted pipelines with modular collaboration patterns, communicate via KV‑Cache to avoid natural‑language bottlenecks, and demonstrate significant accuracy, efficiency, and stability gains across diverse tasks and LLM backbones.
This guide walks through deploying vLLM on Linux with NVIDIA GPUs and Docker Compose, covering service boundaries, host and container checks, model directory validation, image and parameter verification, minimal startup, Compose configuration, API testing, concurrency tuning, multi‑GPU troubleshooting, Nginx exposure, upgrade/rollback procedures, and daily health checks.
The article analyzes how up to 62% of tokens in AI agents are redundantly recomputed, explains the limits of prefix caching, and shows how LMCache’s separate‑process architecture and CacheBlend technique dramatically improve KV‑cache hit rates and inference performance.
HYPIC combines position‑independent caching with hybrid‑attention LLMs, reducing first‑token latency by 3.25× and sustaining QPS by 1.66× while keeping quality loss under 2 points, through a segment‑cumulative transition operator, a seam‑window fix for full‑attention layers, and segment‑parallel execution.
The vLLM 0.25 update adds dozens of new model architectures, expands support to AMD ROCm, CPUs, Intel XPU and IBM Power, fixes speculative decoding for reasoning models, and introduces a suite of performance, KV‑cache, quantization, and API enhancements for large‑scale LLM inference.
The article presents a comprehensive, production‑ready guide for the L1 layer of AI infrastructure, detailing how model gateways unify calls, intelligent routing selects the optimal model, inference engines maximize GPU throughput, and quantization and KV‑Cache techniques dramatically cut costs while maintaining performance.
The article analyzes a recent multi‑institution paper that maps the design space of heterogeneous Prefill‑Decode LLM inference, identifies three core boundary decisions, presents nine deployment best practices, and validates them with a production token‑factory case on MuXi C600 and NVIDIA Hopper GPUs.
This article demonstrates how Hologres AI Function enables end‑to‑end text classification directly in the database using a single SQL call, covering data preparation, prompt engineering, batch inference, accuracy evaluation (up to 95%), and cost analysis with KV‑Cache optimization that reduces token charges to as low as 0.11 CNY for 200 reviews.
The article walks through every stage of LLM inference—from tokenization and embedding, through transformer layers, prefill (compute‑bound) and decode (memory‑bound) phases, KV‑cache management, attention redesign, quantization, and serving infrastructure—explaining how each step consumes time and resources and how to identify bottlenecks.
The article explains why large‑model inference services frequently hit GPU memory limits, breaks down static vs. dynamic memory consumption, shows how KV‑Cache, request length, and concurrency amplify usage, and provides a step‑by‑step troubleshooting and mitigation workflow for production environments.
Although a KV‑cache eviction strategy can discard 90% of tokens, GPU memory usage stays almost unchanged because paged‑attention memory blocks remain occupied and fast attention kernels discard the full score matrix, preventing effective memory release.
This article analyzes how the xLLM open‑source LLM inference engine tackles the coexistence of multiple priority levels and strict SLO latency targets by introducing a dynamic, SLO‑aware batch scheduler and a PD‑separation architecture that improve throughput and SLO satisfaction across diverse workloads.
The article explains how NVIDIA Dynamo tackles the production bottlenecks of Agentic AI by using KV‑Cache‑aware routing, a three‑stage multimodal inference architecture, and intelligent cache scheduling on Kubernetes to improve multi‑node throughput (MFU) while maintaining latency SLAs.
Developers deploying Agentic AI face multi‑turn latency caused by repeated token recomputation, KV‑cache eviction, and cold‑starts, and NVIDIA Dynamo 1.1 addresses these issues with KV‑cache‑aware routing, multi‑level cache offload, priority scheduling, and Prefill/Decode separation, as demonstrated in an upcoming Kubernetes‑based live session.
A systematic ICML 2026 study shows that sharing the K and V projection matrices in Transformers reduces KV cache size by half while incurring less than 5% perplexity degradation, offering a simple, retrain‑once solution for long‑context and edge inference.
A joint effort by Baidu Baige and Fudan University introduces the LU‑KV framework, which treats KV‑cache budget allocation as a global combinatorial optimization problem, achieving only 0.52% relative performance loss at 80% compression and establishing a new efficiency‑accuracy SOTA on LongBench.
The article explains why LLM inference is memory‑bound, introduces vLLM’s three core optimizations—Continuous Batching, PagedAttention, and Prefix Caching—shows how to launch a vLLM server, run Python code to benchmark performance, and examines KV‑Cache memory usage with concrete numbers.
DeepSeek‑V4 reaches a million‑token context window by aggressively compressing its KV‑cache and employing a hybrid attention scheme that combines Compressed Sparse Attention (CSA) for selective top‑k retrieval with Heavily Compressed Attention (HCA) for full‑attention over heavily merged entries, alongside mixed‑precision storage and other engineering optimizations.
OSCAR presents an attention‑aware rotation scheme that compresses KV caches to true 2‑bit, cutting memory usage by up to 8× and boosting decode throughput by up to 7×, while preserving inference quality within a few points of BF16 across multiple models and long‑context benchmarks, outperforming TurboQuant.
The article analyzes the KV‑Cache memory bottleneck of long‑context Transformers, introduces the KV‑CAT (KV‑Compression Aware Training) approach that simulates cache compression during pre‑training, and presents experiments showing unchanged base abilities while dramatically improving post‑training compression, retrieval and long‑text QA performance.
The vLLM 0.22 stable release introduces production‑grade DeepSeek V4 support, massive kernel fusions, up to 10‑20× speedups, Batch Invariance with 28.9% latency gain, a Rust front‑end, multi‑level KV cache offload that can double context length, and broad hardware coverage across NVIDIA, AMD, CPU and RISC‑V, making it a pivotal upgrade for inference infrastructure teams.
MIT researchers introduce Attention Matching, a latent‑space KV‑cache compaction technique that reduces large‑language‑model memory usage up to 50‑fold with negligible precision loss, outperforming token‑pruning, summarization, and prior compaction methods across benchmarks like QuALITY, LongHealth, and AIME‑2025.
OSCAR, a new attention‑aware 2‑bit KV cache quantization method, cuts KV memory by up to 8×, delivers up to 3× decode speedup and 7× throughput gain, and matches BF16 accuracy across 4B‑32B models on diverse long‑context reasoning tasks, surpassing TurboQuant.
The article analyzes the architectural challenges of large‑model inference in the Agent era—such as memory‑intensive MLA structures, MoE communication overhead, exploding KV‑Cache size, and tool‑call accuracy—and presents a series of engineering solutions including hierarchical KV‑Cache pooling, sequence parallelism, offloading strategies, and chip‑level adaptations to achieve higher throughput and lower token costs.
DeepSeek’s 75% permanent API price reduction is analyzed as a strategic move to shrink KV‑cache memory, lower hardware dependence, trigger a demand surge, reshape the AI hardware ecosystem, and capture an estimated $10 trillion market opportunity.
DeepSeek’s recent V4 release shows that as model inference becomes cheaper, the dominant expenses are moving to system‑level components such as KV cache, memory, storage, compilers, scheduling, hardware adapters, and the emerging Agent Harness layer, reshaping AI infrastructure economics.
This article analyzes the hidden pitfalls of Qwen's official Jinja chat template, explains how the community‑maintained Qwen‑Fixed‑Chat‑Templates v19 fixes rendering errors, KV‑Cache loss, token waste and agent dead‑locks, and provides step‑by‑step installation instructions for LM Studio, llama.cpp, vLLM and MLX.
The article surveys recent open‑weight LLM releases—Gemma 4, Laguna XS.2, ZAYA1‑8B and DeepSeek V4—detailing how KV‑cache sharing, per‑layer embeddings, layer‑wise attention budgeting, compressed convolutional attention and manifold‑constrained hyper‑connections dramatically reduce memory and compute for ultra‑long contexts while preserving model quality.
The vLLM 0.21.0 release brings five major updates—including Transformers v4 deprecation, a C++20 build requirement, KV offload with hybrid memory, speculative decoding that respects thinking budgets, and a Blackwell token‑speed backend—while offering detailed upgrade guidance for different user groups.
This article breaks down the key algorithms that power large‑language models—Transformer, Mixture‑of‑Experts, Flash Attention, KV‑Cache, Multi‑Token Prediction, quantization, Chain‑of‑Thought and Retrieval‑Augmented Generation—explaining how each contributes to the performance of ChatGPT, GPT‑4 and DeepSeek.
Through ten experiments on three LLMs, this study measures TurboQuant’s 3‑bit KV‑cache compression, revealing that while quality remains strong, speed gains vary by model, memory savings depend on implementation, and attention‑entropy analysis explains why 2‑bit compression degrades performance.
Redis creator Salvatore Sanfilippo (antirez) released DS4, a Metal‑only C inference engine tailored for DeepSeek V4 Flash on high‑end Macs, featuring narrow model focus, 2‑bit quantization, disk‑based KV cache, benchmark speeds around 26 tokens/s, and a dual OpenAI/Anthropic compatible server.
The article explains why large‑language‑model inference is bottlenecked by memory‑bandwidth, then details Google’s Gemma 4 MTP technique—using a small draft model with speculative decoding and shared KV‑Cache—to parallelize token prediction, achieving up to three‑fold speed gains without any loss in output quality, and provides step‑by‑step local deployment instructions.
This guide walks through deploying the DeepSeek‑V4‑Flash model on Ascend NPU using Kthena’s ModelRoute, detailing the Prefill‑Decode (P/D) separation architecture, KV cache transfer via Mooncake, configuration of ModelServing and ModelRoute resources, and flexible scaling of Prefill and Decode replicas for optimal performance.
The article explains that AI agents’ forgetting and hallucinations stem from token‑level attention scores causing key‑value cache eviction before retrieval, then surveys KV‑cache basics, naive growth, streaming‑LLM windowing, SnapKV’s attention‑guided compression, token‑retention studies, Memory Sparse Attention, compares these methods, and discusses practical system pitfalls and design implications.
The article dissects DeepSeek‑V4’s local deployment using vLLM, explaining the steep hardware requirements, the complex heterogeneous KV‑cache architecture, and the aggressive kernel‑fusion and multi‑stream optimizations that together make high‑context inference both memory‑intensive and engineering‑heavy.
The DeepSeek V4 technical report shows how a 1 million‑token context forces a redesign of attention, KV‑cache, optimizer, quantization and inference budgeting, turning long‑context capability from a costly showcase into a production‑ready feature for agents, search and Chinese professional tasks.
This article explains the mechanics of prompt‑caching for large language models, breaks down static versus dynamic context, details KV‑cache operation and its pricing, and shows how Claude Code’s 30‑minute programming session reached a 92% cache hit rate that reduced inference costs by 81%, concluding with three production‑grade design rules.
This open‑source tutorial breaks down large language model internals into 11 detailed topics—covering BPE tokenization, attention mathematics, backpropagation, transformer architecture, KV‑Cache, Paged and Flash Attention, and frontier techniques—each with numeric derivations and Python code, making it ideal for developers and interview preparation.
This article reviews the DeepLearning.ai short course on SGLang, explains why large‑language‑model inference is slow, details how KV Cache reduces the computation from O(n²) to O(n), introduces RadixAttention for cross‑request caching, and presents code examples and benchmark results showing up to 10× speedup in real‑world RAG scenarios.
The article explains what tokens are, how different models tokenize text, the role of token embeddings, positional encoding, self‑attention, KV cache, and why output tokens cost far more than input tokens, while also covering pricing differences and prompt‑caching savings across major LLM providers.
The article explains how KV cache eliminates redundant computations in autoregressive LLM generation, detailing the attention mechanism, the O(n²) waste of recomputing K and V, the cache‑based solution, its impact on time‑to‑first‑token, and the memory‑vs‑speed trade‑off.
The article explains why relying solely on handcrafted prompts leads to hallucinations in LLM agents and presents six concrete context‑engineering practices—XML isolation, hierarchical ordering, KV caching, vector reranking, async memory compression, and minimal few‑shot examples—illustrated with a full e‑commerce refund‑handling case study.
This article walks through constructing Mini-vLLM, a from‑scratch LLM inference engine that tackles the O(N²) attention cost with KV‑cache, boosts throughput via dynamic batching, adds observability with Prometheus/Grafana, supports gRPC, and scales across multiple workers, with benchmark numbers demonstrating its CPU‑only performance.
The article explains the KV Cache mechanism that stores previously computed key/value vectors to avoid redundant Transformer calculations, delivering roughly a 5× speedup, while also detailing why generating output tokens is far more expensive than processing input tokens due to serial generation and memory trade‑offs.
Google’s newly released TurboQuant algorithm compresses KV‑Cache from 16‑bit to 3‑bit, slashing memory usage to one‑sixth while preserving zero accuracy loss, dramatically accelerating large‑language‑model inference on GPUs and reshaping the memory market.
Google's TurboQuant paper shows KV cache compression up to 6.4× with minimal quality loss, causing DRAM and SSD stocks to tumble, while the open‑source community reproduces the method in under two days and Anthropic and OpenAI add powerful developer‑focused AI features.
The article reviews how the leading LLM inference frameworks—oMLX, mlx‑vlm, llama.cpp, and vLLM—are integrating Google’s TurboQuant compression, showing up to 79% KV‑cache memory reduction, near‑full‑precision decoding speed, and detailed integration steps for each project.
The article explains how Prompt Caching leverages vLLM's PagedAttention and block‑level hashing to reuse KV cache across identical prefixes, dramatically cutting LLM inference latency and cost, and provides concrete engineering tips for maximizing cache hit rates.
TurboQuant, a new technique from Google Research, dramatically compresses key‑value caches by up to six times without precision loss, using PolarQuant and QJL algorithms to transform vectors into polar coordinates and apply quantized Johnson‑Lindenstrauss transforms, thereby boosting inference speed and enabling longer context handling for large language models.
Google’s TurboQuant reduces KV‑Cache memory by up to 4.6×, speeds 3‑bit attention computation up to 8× on H100, and delivers near‑zero accuracy loss across long‑context benchmarks, with open‑source implementations for Metal, vLLM and llama.cpp.
AI coding can generate code ten times faster, yet developers spend 70% of their time on non‑coding tasks such as testing, deployment and review, turning AI into a new bottleneck; the proposed solution, Harness Engineering, equips models with agents, KV‑Cache and multi‑agent workflows so engineers shift from writing code to designing AI‑friendly environments and orchestrating full‑lifecycle development.
This article walks through MiniMind's Attention.forward implementation, explaining why Q, K, and V are created, how tensors are reshaped for multi‑head attention, the role of masks, KV cache, GQA, and how each token aggregates information from the entire context.
This guide explains the five common sources of GPU memory consumption in large‑model inference services, provides a step‑by‑step diagnosis workflow—from static usage and KV‑Cache analysis to concurrency and K8s scheduling—offers concrete command‑line checks, scripts, configuration examples, and actionable remediation and monitoring recommendations.
LMCache separates the KV cache from a vLLM instance into a shared service, dramatically cutting first‑token latency for repeated text, enabling multiple GPU instances to reuse cached vectors, improving hardware utilization, and supporting use cases such as long‑document QA, multi‑GPU load balancing, and prompt‑engineering, with a quick Docker‑based demo.
The article traces five eras of KV cache management for LLM inference—from its absence before Transformers to the emerging unified hybrid memory architecture—comparing vLLM, SGLang, and TensorRT‑LLM and offering a decision framework for selecting the right solution in various deployment scenarios.
The article analyzes the KV‑Cache storage I/O bottleneck that limits agentic LLM inference, introduces the DualPath architecture with a storage‑to‑decode data path and RDMA‑based scheduling, and shows up to 1.87× offline and 1.96× online throughput gains on large‑scale GPU clusters.
DeepSeek’s new DualPath inference framework, co‑developed with leading Chinese universities, decouples compute from KV‑Cache memory access to eliminate I/O stalls in multi‑round agentic workloads, delivering up to nearly 2× higher throughput and dramatically reducing job‑completion time across several large‑scale LLMs.
The article explains how Amazon SageMaker's Sticky Session routing creates session affinity, allowing KV cache reuse across requests, which eliminates redundant computation, reduces latency, and improves memory efficiency for multi‑turn LLM applications.
The article analyzes the planning-with-files technique — three Markdown files for task planning, knowledge storage, and state tracking — and its alignment with Manus's context engineering strategies (reduce, offload, isolate context), while detailing Manus's tech stack including multi-model routing, E2B sandbox, MCP protocol, and KV cache optimization.
This article details the engineering challenges and solutions for deploying a 3.5 billion‑parameter MoE LLM in Taobao's search relevance pipeline, covering large‑batch scheduling, dynamic load balancing, intra‑batch KV‑Cache reuse, and MoE kernel tuning to meet sub‑second latency requirements.
DeepSeek’s recent FlashMLA update introduces the new MODEL1, featuring a tighter KV-Cache layout, an extra two-stage cache, and a fixed 512×512 head dimension, with four code changes detailed in a public GitHub commit and illustrated by comparative diagrams.
This article systematically covers LLM inference optimization across performance metrics (TTFT, TPOT, throughput, goodput, MFU, MBU), model-level techniques (quantization, distillation, pruning, speculative decoding, attention optimizations), and service-level strategies (continuous batching, prefill/decode separation, prompt caching, parallelism) to improve latency, throughput, and cost-efficiency.
Tencent WeChat AI introduces WeDLM, a diffusion language model that works with standard causal attention and KV caching, achieving up to ten‑fold speedups over autoregressive models while maintaining or improving generation quality across math reasoning and open‑ended tasks.
Jeff Dean highlighted speculative decoding as a lossless inference acceleration technique that can boost large language model throughput by 2–3×, and the article breaks down its core concepts—including parallel token verification, draft‑target model collaboration, rejection sampling theory, and practical optimizations such as continuous batching and tree‑based verification.
This article explains why KV‑Cache hit rate is critical for large‑model inference, describes vLLM's automatic prefix caching, outlines the distributed cache challenges, and provides a step‑by‑step guide to deploying Alibaba Cloud ACK Gateway with Inference Extension's precise‑mode prefix‑cache‑aware routing, backed by benchmark results.
The article breaks down the principles of KV caching in large language models, explaining how Q/K/V behavior differs between training and inference, the role of prompts, cache size trade‑offs, and the complexities of concurrent inference, all backed by concrete examples and references.
The article explains that a context window defines an LLM's token‑level memory capacity, shows how longer windows cause quadratic computation growth, introduces KV Cache as a way to extend context without exploding resources, and covers advanced techniques like Ring Attention, NIAH benchmarking, and attention decay in long sequences.
This article examines how to evolve AI prompt engineering into full‑stack context and environment engineering, detailing six practical design patterns from the Manus system, the limits of Vibe Coding, the Spec‑Driven development workflow, and concrete steps to give models a persistent, controllable world for long‑term tasks.
The article analyzes how generative AI workloads are reshaping the storage market into a multi‑year Memory Supercycle, detailing demand spikes from Mid‑Training checkpoints, synthetic data, KV‑Cache offload and multimodal video models, while supply is strained by HBM production and geopolitical factors.
