In this new interview, our CEO & co-founder @JunchenJiang explains why KV cache — the internal memory of LLMs, is becoming the 𝗻𝗲𝘅𝘁 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 layer for AI, and how @tensormesh tackles large-scale inference.
🎥 Watch the full interview: youtu.be/zHW4Zzd7pjI
#LLMInference #KVCache #OpenSource #PyTorch
🎥 Watch the full interview: youtu.be/zHW4Zzd7pjI
#LLMInference #KVCache #OpenSource #PyTorch
January 6, 2026 at 5:05 PM
In this new interview, our CEO & co-founder @JunchenJiang explains why KV cache — the internal memory of LLMs, is becoming the 𝗻𝗲𝘅𝘁 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 layer for AI, and how @tensormesh tackles large-scale inference.
🎥 Watch the full interview: youtu.be/zHW4Zzd7pjI
#LLMInference #KVCache #OpenSource #PyTorch
🎥 Watch the full interview: youtu.be/zHW4Zzd7pjI
#LLMInference #KVCache #OpenSource #PyTorch
🎓 Scalable Machine Learning and Large Language Model inference
Your #PhDOpportunity in #AIResearch: Apply now for one of the 8 possible PhD topics in the area #ScalableML and #LLMinference!
👉 scads.ai/about-us/job-offers/research-topics/
Your #PhDOpportunity in #AIResearch: Apply now for one of the 8 possible PhD topics in the area #ScalableML and #LLMinference!
👉 scads.ai/about-us/job-offers/research-topics/
March 24, 2025 at 2:13 PM
🎓 Scalable Machine Learning and Large Language Model inference
Your #PhDOpportunity in #AIResearch: Apply now for one of the 8 possible PhD topics in the area #ScalableML and #LLMinference!
👉 scads.ai/about-us/job-offers/research-topics/
Your #PhDOpportunity in #AIResearch: Apply now for one of the 8 possible PhD topics in the area #ScalableML and #LLMinference!
👉 scads.ai/about-us/job-offers/research-topics/
Just saw AIPerf’s latest benchmark—Qwen3‑0.6B crushing LLM inference speeds. If you care about real‑world AI performance, this dive is a must‑read. Curious how it stacks? Check it out! #AIPerf #Qwen3 #LLMInference
🔗 aidailypost.com/news/aiperf-...
🔗 aidailypost.com/news/aiperf-...
September 18, 2026 at 7:38 PM
Just saw AIPerf’s latest benchmark—Qwen3‑0.6B crushing LLM inference speeds. If you care about real‑world AI performance, this dive is a must‑read. Curious how it stacks? Check it out! #AIPerf #Qwen3 #LLMInference
🔗 aidailypost.com/news/aiperf-...
🔗 aidailypost.com/news/aiperf-...
I was on #TheAIKubernetes with William the CEO. We discussed LoadBalancers for LLMs, Agent Identity and resource mgmt
Watch our conversation: lnkd.in/dE9-p-r6
#TheAIKubernetesShow #Kubernetes #AgenticAI #LLMInference
Watch our conversation: lnkd.in/dE9-p-r6
#TheAIKubernetesShow #Kubernetes #AgenticAI #LLMInference
July 21, 2026 at 2:57 PM
I was on #TheAIKubernetes with William the CEO. We discussed LoadBalancers for LLMs, Agent Identity and resource mgmt
Watch our conversation: lnkd.in/dE9-p-r6
#TheAIKubernetesShow #Kubernetes #AgenticAI #LLMInference
Watch our conversation: lnkd.in/dE9-p-r6
#TheAIKubernetesShow #Kubernetes #AgenticAI #LLMInference
NVIDIA’s Feynman roadmap suggests a shift from training-centric GPUs toward latency-optimized, inference-scale systems.
www.buysellram.com/blog/nvidia-...
#InferenceSovereignty #LLMInference #NVIDIA #Feynman #HBM4 #SRAM #AIInfrastructure #GPU #GTC2026 #DeterministicCompute #LPX #GroqLPU
www.buysellram.com/blog/nvidia-...
#InferenceSovereignty #LLMInference #NVIDIA #Feynman #HBM4 #SRAM #AIInfrastructure #GPU #GTC2026 #DeterministicCompute #LPX #GroqLPU
NVIDIA Next-Gen Feynman: Beyond Training, Toward Inference Sovereignty
Prepare for NVIDIA GTC 2026. Explore the shift to Inference Sovereignty, the 1.6nm Feynman architecture, deterministic LPX cores, and the future of 100M IOPS AI storage.
www.buysellram.com
March 1, 2026 at 10:54 PM
NVIDIA’s Feynman roadmap suggests a shift from training-centric GPUs toward latency-optimized, inference-scale systems.
www.buysellram.com/blog/nvidia-...
#InferenceSovereignty #LLMInference #NVIDIA #Feynman #HBM4 #SRAM #AIInfrastructure #GPU #GTC2026 #DeterministicCompute #LPX #GroqLPU
www.buysellram.com/blog/nvidia-...
#InferenceSovereignty #LLMInference #NVIDIA #Feynman #HBM4 #SRAM #AIInfrastructure #GPU #GTC2026 #DeterministicCompute #LPX #GroqLPU
DeepSeek's new open-source inference optimizations claim 60-85% faster LLM generation and drastically lower costs. This could reshape AI development, but the real question is: can *you* actually use it without a server farm?
https://www.tpp.blog/2p5jr0a
#technology #deepseek #llminference
https://www.tpp.blog/2p5jr0a
#technology #deepseek #llminference
June 27, 2026 at 1:12 PM
DeepSeek's new open-source inference optimizations claim 60-85% faster LLM generation and drastically lower costs. This could reshape AI development, but the real question is: can *you* actually use it without a server farm?
https://www.tpp.blog/2p5jr0a
#technology #deepseek #llminference
https://www.tpp.blog/2p5jr0a
#technology #deepseek #llminference
Run huge LLMs on your Apple Silicon! Hypura intelligently manages memory across GPU, RAM, and NVMe, making previously impossible models available. See how it works!
https://thepixelspulse.com/posts/hypura-llm-inference-scheduler-apple-silicon/
#hypura #applesilicon #llminference
https://thepixelspulse.com/posts/hypura-llm-inference-scheduler-apple-silicon/
#hypura #applesilicon #llminference
March 24, 2026 at 8:14 PM
Run huge LLMs on your Apple Silicon! Hypura intelligently manages memory across GPU, RAM, and NVMe, making previously impossible models available. See how it works!
https://thepixelspulse.com/posts/hypura-llm-inference-scheduler-apple-silicon/
#hypura #applesilicon #llminference
https://thepixelspulse.com/posts/hypura-llm-inference-scheduler-apple-silicon/
#hypura #applesilicon #llminference
TurboFieldfare sets a ~2 GB memory budget first and then rebuilds the Gemma 4 26B-A4B runtime, its per-layer expert cache, and even the installer to live inside it on an 8 GB Mac: https://github.com/drumih/turbo-fieldfare
#Swift #Metal #LlmInference
#Swift #Metal #LlmInference
August 29, 2026 at 4:55 PM
TurboFieldfare sets a ~2 GB memory budget first and then rebuilds the Gemma 4 26B-A4B runtime, its per-layer expert cache, and even the installer to live inside it on an 8 GB Mac: https://github.com/drumih/turbo-fieldfare
#Swift #Metal #LlmInference
#Swift #Metal #LlmInference
kimi-k3-in-c runs a 2.78-trillion-parameter Kimi K3 on one CPU in 8.24 GB of RAM, with byte-identical output from 8 GB up to 224 GB, so memory buys speed rather than eligibility: https://github.com/FareedKhan-dev/kimi-k3-in-c
#C #LlmInference #CpuInference
#C #LlmInference #CpuInference
August 28, 2026 at 10:48 PM
kimi-k3-in-c runs a 2.78-trillion-parameter Kimi K3 on one CPU in 8.24 GB of RAM, with byte-identical output from 8 GB up to 224 GB, so memory buys speed rather than eligibility: https://github.com/FareedKhan-dev/kimi-k3-in-c
#C #LlmInference #CpuInference
#C #LlmInference #CpuInference
vLLM vs Ollama: Which Inference Server You Actually Need in 2026
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and O…
#vllm #ollama #llminference
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and O…
#vllm #ollama #llminference
vLLM vs Ollama: Which Inference Server You Actually Need in 2026
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and Ollama solve it in opposite ways.
www.alekseialeinikov.com
September 15, 2026 at 6:47 AM
vLLM vs Ollama: Which Inference Server You Actually Need in 2026
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and O…
#vllm #ollama #llminference
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and O…
#vllm #ollama #llminference
Can an old consumer PC run large LLMs better than we assume? My DeepSeek V4 experiments challenged the idea that disk streaming was the real performance wall. #llminference
Your Old PC Can Run LLMs: The Real Bottleneck Is Optimization
hackernoon.com
August 27, 2026 at 4:36 AM
Can an old consumer PC run large LLMs better than we assume? My DeepSeek V4 experiments challenged the idea that disk streaming was the real performance wall. #llminference
#LLMInference #MLOps #AWS #AIInfrastructure #MachineLearning (4/4)
August 26, 2026 at 8:05 AM
#LLMInference #MLOps #AWS #AIInfrastructure #MachineLearning (4/4)
#OpenAI’s #Jalapeño, a self-designed #ASIC for #LLMinference, outperforms #Nvidia’s #Blackwell and other competitors in #tokenthroughput per watt. Built with Broadcom, the chip uses HBM4 memory and a generalised architecture, excelling in both low-latency and high-throughput scenarios. While…
tech news ᳇ eicker.news (@technews@eicker.news)
#OpenAI’s #Jalapeño, a self-designed #ASIC for #LLMinference, outperforms #Nvidia’s #Blackwell and other competitors in #tokenthroughput per watt. Built with Broadcom, the chip uses HBM4 memory and a generalised architecture, excelling in both low-latency and high-throughput scenarios. While comparisons to Blackwell are incomplete, Jalapeño’s performance against Vera Rubin, which also uses HBM4, is noteworthy. https://newsletter.semianalysis.com/p/openai-jalapeno-better-than-nvidia?eicker.news #tech #news #ainews
eicker.news
August 26, 2026 at 9:29 AM
#OpenAI’s #Jalapeño, a self-designed #ASIC for #LLMinference, outperforms #Nvidia’s #Blackwell and other competitors in #tokenthroughput per watt. Built with Broadcom, the chip uses HBM4 memory and a generalised architecture, excelling in both low-latency and high-throughput scenarios. While…
KVBoost just slashed LLM inference latency by reusing KV cache with a clever dual‑hash trick. See how Qwen2.5‑3B and other transformers get faster prefix caching on HuggingFace. Dive into the details! #KVBoost #LLMinference #DualHash
🔗 aidailypost.com/news/kvboost...
🔗 aidailypost.com/news/kvboost...
August 25, 2026 at 10:18 PM
KVBoost just slashed LLM inference latency by reusing KV cache with a clever dual‑hash trick. See how Qwen2.5‑3B and other transformers get faster prefix caching on HuggingFace. Dive into the details! #KVBoost #LLMinference #DualHash
🔗 aidailypost.com/news/kvboost...
🔗 aidailypost.com/news/kvboost...
Prefill/decode disaggregation can worsen tail latency through queue imbalance and KV transfer. Measure the full TTFT path and use load-aware routing. #llminference
Prefill/Decode Disaggregation Can Make Tail Latency Worse
hackernoon.com
August 14, 2026 at 5:00 PM
Prefill/decode disaggregation can worsen tail latency through queue imbalance and KV transfer. Measure the full TTFT path and use load-aware routing. #llminference
High-Throughput LLM Inference — Discover secrets of vLLM, a record-breaking LLM inference system for AI development and deployment
Read more →
#AIDevelopment #LLMInference #HighThroughput
Read more →
#AIDevelopment #LLMInference #HighThroughput
High-Throughput LLM Inference
Discover secrets of vLLM, a record-breaking LLM inference system for AI development and deployment
airanked.dev
August 7, 2026 at 8:00 AM
High-Throughput LLM Inference — Discover secrets of vLLM, a record-breaking LLM inference system for AI development and deployment
Read more →
#AIDevelopment #LLMInference #HighThroughput
Read more →
#AIDevelopment #LLMInference #HighThroughput
Google’s TurboQuant is being positioned as a breakthrough that could finally break the AI “memory wall”—but the reality is more nuanced.
www.buysellram.com/blog/will-go...
#AI #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #LLMInference #MemoryBottleneck #ModelEfficiency #DataCenter
www.buysellram.com/blog/will-go...
#AI #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #LLMInference #MemoryBottleneck #ModelEfficiency #DataCenter
Will Google's TurboQuant AI Compression Finally Demolish the AI Memory Wall?
Will TurboQuant end the HBM shortage? Explore Google’s 6x KV cache compression, the Jevons Paradox, and how to manage GPU assets as the AI Memory Wall moves.
www.buysellram.com
March 28, 2026 at 1:25 PM
Google’s TurboQuant is being positioned as a breakthrough that could finally break the AI “memory wall”—but the reality is more nuanced.
www.buysellram.com/blog/will-go...
#AI #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #LLMInference #MemoryBottleneck #ModelEfficiency #DataCenter
www.buysellram.com/blog/will-go...
#AI #TurboQuant #Google #AIMemoryWall #AICompression #KVCache #LLMInference #MemoryBottleneck #ModelEfficiency #DataCenter
Our team at 𝗥𝗲𝗱 𝗛𝗮𝘁 𝗔𝗜 been working closely with both the 𝗞𝗦𝗲𝗿𝘃𝗲 and 𝗹𝗹𝗺-𝗱 communities to introduce a new 𝗟𝗟𝗠𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 CRD in KServe — a unified API that delivers a consistent serving experience across use cases and maturity levels.
August 11, 2025 at 3:45 PM
Our team at 𝗥𝗲𝗱 𝗛𝗮𝘁 𝗔𝗜 been working closely with both the 𝗞𝗦𝗲𝗿𝘃𝗲 and 𝗹𝗹𝗺-𝗱 communities to introduce a new 𝗟𝗟𝗠𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 CRD in KServe — a unified API that delivers a consistent serving experience across use cases and maturity levels.
How do you bridge research and product?
In this clip, CTO 𝗬𝗶𝗵𝘂𝗮 𝗖𝗵𝗲𝗻𝗴 and Chief Scientist @this_will_echo discuss why resilient AI infrastructure requires tight collaboration between research and product teams from day one.
🎥 Full interview:
👉 lnkd.in/gk4e7bJS
#LLMInference #Tensormesh
In this clip, CTO 𝗬𝗶𝗵𝘂𝗮 𝗖𝗵𝗲𝗻𝗴 and Chief Scientist @this_will_echo discuss why resilient AI infrastructure requires tight collaboration between research and product teams from day one.
🎥 Full interview:
👉 lnkd.in/gk4e7bJS
#LLMInference #Tensormesh
May 19, 2026 at 4:04 PM
How do you bridge research and product?
In this clip, CTO 𝗬𝗶𝗵𝘂𝗮 𝗖𝗵𝗲𝗻𝗴 and Chief Scientist @this_will_echo discuss why resilient AI infrastructure requires tight collaboration between research and product teams from day one.
🎥 Full interview:
👉 lnkd.in/gk4e7bJS
#LLMInference #Tensormesh
In this clip, CTO 𝗬𝗶𝗵𝘂𝗮 𝗖𝗵𝗲𝗻𝗴 and Chief Scientist @this_will_echo discuss why resilient AI infrastructure requires tight collaboration between research and product teams from day one.
🎥 Full interview:
👉 lnkd.in/gk4e7bJS
#LLMInference #Tensormesh
SentenceKV compresses token KV pairs into sentence‑level vectors, cutting memory use and keeping latency stable; on the PG‑19 benchmark it lowered memory footprint and matched perplexity. https://getnews.me/sentencekv-improves-llm-inference-with-sentence-level-kv-caching/ #sentencekv #llminference
October 1, 2025 at 1:21 PM
SentenceKV compresses token KV pairs into sentence‑level vectors, cutting memory use and keeping latency stable; on the PG‑19 benchmark it lowered memory footprint and matched perplexity. https://getnews.me/sentencekv-improves-llm-inference-with-sentence-level-kv-caching/ #sentencekv #llminference
Shift Parallelism toggles between tensor and sequence parallelism, delivering up to 1.51× faster response times and about 50% higher token throughput in batch workloads. Read more: https://getnews.me/shift-parallelism-improves-llm-inference-speed-and-throughput/ #llminference #parallelism
September 24, 2025 at 7:40 AM
Shift Parallelism toggles between tensor and sequence parallelism, delivering up to 1.51× faster response times and about 50% higher token throughput in batch workloads. Read more: https://getnews.me/shift-parallelism-improves-llm-inference-speed-and-throughput/ #llminference #parallelism
Study shows throughput‑oriented LLM inference on opportunistic GPUs cuts execution time by 98.1% versus static allocation via pervasive context management. Read more: https://getnews.me/throughput-oriented-llm-inference-on-opportunistic-gpu-clusters/ #llminference #opportunisticgpu
September 18, 2025 at 4:39 PM
Study shows throughput‑oriented LLM inference on opportunistic GPUs cuts execution time by 98.1% versus static allocation via pervasive context management. Read more: https://getnews.me/throughput-oriented-llm-inference-on-opportunistic-gpu-clusters/ #llminference #opportunisticgpu
連続バッチ処理における非同期性の解放：LLM推論スループットの限界突破
連続バッチ処理の非同期化でLLM推論を最適化。
#LLMInference #AsynchronousProgramming #ContinuousBatching #PerformanceOptimization #DeepLearningSystems
連続バッチ処理の非同期化でLLM推論を最適化。
#LLMInference #AsynchronousProgramming #ContinuousBatching #PerformanceOptimization #DeepLearningSystems
連続バッチ処理における非同期性の解放：LLM推論スループットの限界突破
連続バッチ処理の非同期化でLLM推論を最適化。
ai.warp-studio.com
May 14, 2026 at 4:06 PM
連続バッチ処理における非同期性の解放：LLM推論スループットの限界突破
連続バッチ処理の非同期化でLLM推論を最適化。
#LLMInference #AsynchronousProgramming #ContinuousBatching #PerformanceOptimization #DeepLearningSystems
連続バッチ処理の非同期化でLLM推論を最適化。
#LLMInference #AsynchronousProgramming #ContinuousBatching #PerformanceOptimization #DeepLearningSystems
New trick: researchers hide a mask token right inside the LLM weights, letting the model crank out up to 3× faster token generation with parallel speculation. Curious how? Dive in for the details! #LLMinference #SpeculativeDecoding #ModelAcceleration
🔗 aidailypost.com/news/researc...
🔗 aidailypost.com/news/researc...
February 23, 2026 at 6:11 PM
New trick: researchers hide a mask token right inside the LLM weights, letting the model crank out up to 3× faster token generation with parallel speculation. Curious how? Dive in for the details! #LLMinference #SpeculativeDecoding #ModelAcceleration
🔗 aidailypost.com/news/researc...
🔗 aidailypost.com/news/researc...