GCC/MENA inference teams: TaSQ compresses the KV cache to 1 bit and reports up to 14x larger batches and 1.87x higher peak throughput vs BF16 (SGLang, one RTX 6000 Ada). Bigger batches are not proportional speed, so benchmark. eucax.ai/insights/tasq-and-t… #LLMInference #KVCache

Chunked #KVcache compression introduces dangerous periodic blind spots into #transformer architectures, reveals new research highlighted by @HuggingPapers. Models compressing #token histories at fixed strides reliably retrieve #context at certain boundaries but fail ⚠️catastrophically at intermediate offsets. This phase-dependent #degradation can quietly flip code completions or corrupt reasoning chains without triggering obvious #runtime warnings. Engineers deploying long-context #compression must 🤔re-evaluate fixed-stride strategies to prevent subtle inference failures in mission-critical #GenerativeAI systems. 🔬 x.com/HuggingPapers/status/2…

‼️New paper alert‼️ What does it take to recover the lost accuracy in non-prefix KVCache reuse? Simple averaging sometimes works surprising well! No fine-tunig, zero runtime cost. Article/paper: linkedin.com/pulse/kvprior-e… #RAG #LLM #KVCache #NLP #AI

How much of your inference workload is 𝒂𝒄𝒕𝒖𝒂𝒍𝒍𝒚 new? Tensormesh's #KVcache Hit Rate shows how often cached state is reused instead of recomputed, giving teams visibility into how much repeated work their models are doing. You can't 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑒 𝑟𝑒𝑝𝑒𝑎𝑡𝑒𝑑 𝑤𝑜𝑟𝑘 if you can't see it.

Un #LLM escribe palabra a palabra y cada una tiene que «mirar» todo lo anterior. La #KVCache evita recalcularlo: guarda la K y la V de cada token y las reutiliza. Sin caché, cada paso recalcula más tokens; con caché, uno. El precio: ~128 KiB de memoria/token en un 8B en FP16.

Why KV cache makes LLM generation faster #KVCache #LLM #AIInference #Transformers #GPUServing

GCC/MENA inference teams: SparseEngine makes long-context serving a state-management problem. Reports over 10x throughput with KV eviction and over 2.5x faster decoding vs vLLM at matched concurrency. eucax.ai/insights/sparseengi… #LLMInference #KVCache #AIInfrastructure

That's a wrap on #TheAIConference in SF! Thanks to everyone who stopped by to talk faster, more efficient AI inference. And yes, we met a robot. Say hi to Gerald! 🤖 #AIInfrastructure #KVCache @aiconference

GCC/MENA inference teams: PulseInfer treats sparse KV offloading as an I/O scheduling problem for long-context decoding, reporting up to 4.7x decode throughput vs SGLang without treating host memory as a free lunch. eucax.ai/insights/pulseinfer… #LLMInference #KVCache #AIInfrastructure

62% faster time to first token. 22% more tokens/sec. Leo X-Series puts #KVCache closer to GPUs with fabric-attached memory and Scorpio™ Smart Fabric Switches - supporting PCIe and platform-specific GPUs at rack scale. Learn more: asteralabs.com/products/leo-… #AsteraLabs #AI

Multi Head Attention vs Multi Query Attention #LLM #Transformers #KVCache #MQA #MHA #AIEngineering #Inference

GCC/MENA inference teams: a smaller KV cache is not a faster service. EucaX on why KV-cache work is now a memory-systems discipline. eucax.ai/insights/kv-cache-o… #LLMInference #KVCache #AIInfrastructure

ScaleFlux is aiming an SSD platform at NVIDIA CMX KV cache: 7 to 10+ effective DWPD, 200+ FDP write streams per drive, and 2x lower write amplification in preliminary testing. All ScaleFlux figures. storagereview.com/news/scale… #KVcache #FDP #SSD @ScaleFlux

KIOXIA's first PCIe 6.0 enterprise SSD: the CM10 pairs 332-layer BiCS gen 10 with up to 61.44TB and claims 92% higher sequential read than CM9. Cold-plate liquid cooling on E3.S. storagereview.com/news/kioxi… #KVcache #SSD @KIOXIAAmerica

In Part 2 of our #KVCache series, #TrendForce explores algorithmic breakthroughs by #Google, #NVIDIA, #Meta, and #DeepSeek. What do these leaps mean for future memory demand? 💡More: pse.is/9ejfjn 🔗

FP8 KV cache is underrated. Weights load once, but KV grows with every token and batch. Halving KV from FP16 to FP8 roughly doubles the context you hold at fixed VRAM, and attention is memory-bound so the accuracy hit stays small. #quantization #KVcache #FP8 #inference #LLM

We're pulling 16 DIMMs out of a Dell PowerEdge XE7740 on purpose. With DRAM near a quarter million at list, the question is how much KV cache you can push onto flash instead. Half the RAM, full bandwidth, more stress on the flash, results soon. #KVcache #PowerEdge #SolidigmSSD @solidigm

AI がトレーニングから #inference へシフトすると、#memory 需要にはどのような変化が起きるのでしょうか？注目すべき 2 つのドライバーは、KV キャッシュのオフロードとエージェント型 AI です。詳細はこちら：pse.is/97cx86 🔗 #KVcache #AgenticAI

What happens to #memory demand when AI shifts from training to #inference? KV cache offloading and agentic AI are two drivers worth watching. More: pse.is/97cx86 🔗 #KVcache #AgenticAI