BlemeWho Controls AI Compute? - Opening Voices with Steeve Morin of ZML
PeerTube
BuySellRam.comInference is becoming the primary cost center of AI, and NVIDIA’s Feynman roadmap suggests a shift from training-centric GPUs toward latency-optimized, inference-scale systems.
As real-time agents, copilots, and edge deployments grow, inference sovereignty—where compute is located, how fast it responds, and who controls the hardware—will define the next phase of AI infrastructure.
With NVIDIA GTC 2026 approaching, the key question is whether NVIDIA will formally introduce a new class of inference-focused silicon and fabric to complement its training platforms.
#InferenceSovereignty #LLMInference #AgenticAI #NVIDIA #Feynman #HBM4 #SRAM #AdvancedPackaging #SiliconPhotonics #AIInfrastructure #GPU #GTC2026 #Rubin #Blackwell #DeterministicCompute #LPX #GroqLPU #technology
Alex S.Inference is becoming the primary cost center of AI, and NVIDIA’s Feynman roadmap suggests a shift from training-centric GPUs toward latency-optimized, inference-scale systems.
As real-time agents, copilots, and edge deployments grow, inference sovereignty—where compute is located, how fast it responds, and who controls the hardware—will define the next phase of AI infrastructure.
With NVIDIA GTC 2026 approaching, the key question is whether NVIDIA will formally introduce a new class of inference-focused silicon and fabric to complement its training platforms.
#InferenceSovereignty #LLMInference #AgenticAI #NVIDIA #Feynman #HBM4 #SRAM #AdvancedPackaging #SiliconPhotonics #AIInfrastructure #GPU #GTC2026 #Rubin #Blackwell #DeterministicCompute #LPX #GroqLPU #technology
BuySellRam.comWith NVIDIA GTC 2026 approaching, the key question is whether NVIDIA will formally introduce a new class of inference-focused silicon and fabric to complement its training platforms.
#InferenceSovereignty #LLMInference #AgenticAI #NVIDIA #Feynman #HBM4 #SRAM #AdvancedPackaging #SiliconPhotonics #AIInfrastructure #GPU #GTC2026 #Rubin #Blackwell #DeterministicCompute #LPX #GroqLPU #technology
AI Daily PostResearchers have discovered a clever trick: by embedding a mask token directly into the weight matrix, they can bypass the costly embedding lookup and generate up to three times faster token streams. The method works with parallel computation and speculative decoding, promising big gains for open‑source LLMs. Read on to see how ConfAdapt powers this speed‑up. #LLMinference #SpeculativeDecoding #MultiTokenPrediction #ModelAcceleration
🔗 https://aidailypost.com/news/researchers-embed-mask-token-llm-weights-achieve-3-faster-inference
Run:ai runs on 64 GPUs, handling 10,200 concurrent users while matching the native scheduler’s performance. The benchmark shows how GPU fractioning boosts token throughput for LLM inference, proving that open‑source AI infrastructure can scale efficiently in the cloud. Curious how this works? Read the full study. #GPUFractioning #LLMInference #RunAI #TokenThroughput
🔗 https://aidailypost.com/news/runai-64-gpus-serves-10200-users-matching-native-scheduler
Dr. ThompsonYour GPU isn’t weak.
Your assumptions are. 🧠⚡
How ordinary machines are suddenly running massive LLMs with long memory—without cloud or quantization.
Read the shift 👇
https://medium.com/@rogt.x1997/ollm-and-the-new-physics-of-local-llm-inference-how-memory-hierarchies-not-gpus-decide-what-3927e1e9fe14
#LocalAI #LLMInference #GenAI
https://medium.com/@rogt.x1997/ollm-and-the-new-physics-of-local-llm-inference-how-memory-hierarchies-not-gpus-decide-what-3927e1e9fe14
Running LLMs locally just got a boost: Hyperlink Agent Search on NVIDIA RTX GPUs doubles inference speed, letting you query massive models across your own files in natural language. See how the RTX hardware unlocks faster, more efficient generative AI on your machine. #HyperlinkAgentSearch #NVIDIARTX #GenerativeAI #LLMinference
🔗 https://aidailypost.com/news/hyperlink-agent-search-nvidia-rtx-pcs-doubles-llm-inference-speed
Hacker NewsDefeating Nondeterminism in LLM Inference
https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/
#HackerNews #DefeatingNondeterminism #LLMInference #AIResearch #MachineLearning #TechInnovation
Defeating Nondeterminism in LLM Inference
Reproducibility is a bedrock of scientific progress. However, it’s remarkably difficult to get reproducible results out of large language models. For example, you might observe that asking ChatGPT the same question multiple times provides different results. This by itself is not surprising, since getting a result from a language model involves “sampling”, a process that converts the language model’s output into a probability distribution and probabilistically selects a token. What might be more surprising is that even when we adjust the temperature down to 0This means that the LLM always chooses the highest probability token, which is called greedy sampling. (thus making the sampling theoretically deterministic), LLM APIs are still not deterministic in practice (see past discussions here, here, or here). Even when running inference on your own hardware with an OSS inference library like vLLM or SGLang, sampling still isn’t deterministic (see here or here).