ScitiX

Faster LLM inference isn’t about adding more GPUs.😎 In production, inference bottlenecks are often memory-bound, not compute-bound. As context length and concurrency grow, KV cache pressure saturates memory bandwidth long before GPUs hit peak utilization.

🥂What an epic night! Our #GTC2026 Happy Hour co-hosted with the SGLang team was a massive success! @lmsysorg @sgl_project ⚡️The venue was packed, and the conversations around SGLang's Scalable Multilingual LLM Serving Engine were electric. From tech deep-dives to future trends, the AI community energy was unmatched! Thank you to everyone who came out! Catch you at the next GTC! 👇🎥 #SGLang #NVIDIA #LLM #TechEvent

We didn't just build a cache; we built it for extreme scale. 🔥 Up to 9x lower latency via end-to-end zero-copy. 📈 Linear scalability & full utilization of ALL RDMA NICs. 🚀 3.1x TTFT speedup (at 32K context) vs. no-cache baselines! Built for multi-turn agents and long Chain-of-Thought reasoning, SiMM is already proven in production and deploys easily via K8s. Ready to accelerate your LLM serving and Agentic RL ? try it now! 🔗github.com/scitix/SiMM #LLMs #OpenSource #AI #MachineLearning #KVCache

LLM context lengths have grown 4x in the past year, causing skyrocketing GPU costs and painful Time-To-First-Token (TTFT) latency. Today, we are thrilled to open-source SiMM ⚡️: an ultra-fast, scalable in-memory KV cache engine that breaks the long-context bottleneck! SiMM transforms heavy prefill compute into high-speed I/O retrieval. It provides a scalable and low-latency memory pool for KV cache that seamlessly integrates with leading engines like @sgl_project and @vllm_project . #LLM #OpenSource #AI #MachineLearning #KVCache

Thrilled to partner with @lmsysorg and an amazing crew for this! 🚀 If you're at GTC, come grab a drink and let's talk the future of AI compute. See you in San Jose! 🍻

🛠️K8s down? Don't panic, ask Siclaw. It troubleshoots like a Senior SRE on their best day: 1️⃣ Checks state 2️⃣ Builds hypotheses 3️⃣ Runs concurrent deep-dives 4️⃣ Delivers the fix < 1 min. Why not put it to the test? Try it out here: 👇 siclaw.ai

What makes SiClaw different from OpenClaw? 🔐 Security Governance A strict permission layer between agents and infrastructure: read-only by default, command whitelists, per-action approval for writes, and workspace-isolated credentials to protect production environments. 👥 Team Collaboration Built for enterprise teams with multi-workspace support, multi-user management (SSO/OAuth2), and isolated AgentBox sandboxes for different teams and environments. ⚡ Proactive Operations Integrates with mainstream monitoring and alerting systems to detect anomalies, automatically generate diagnostic plans, and execute remediation actions—moving from reactive to autonomous operations. 🔍 Deep Diagnostics A hypothesis-driven 4-stage diagnostic engine: Context collection → Hypothesis generation → Parallel validation → Root-cause conclusion. Bringing “Deep Research” capabilities into real-world operations. 💬 Join our community: join.slack.com/t/siclaw-sciti…

🚀 We’re building SiClaw, an open-source AIOps platform based on Pi-Agent. SiClaw focuses on Intelligent Infrastructure, DevOps Automation, and Dev–Ops Collaboration. We welcome developers to try it out, share feedback, and contribute! 🌐 Website: siclaw.ai 💻 GitHub: github.com/scitix/siclaw #AIOps #DevOps #OpenSource

In AI infrastructure, comparing raw GPU models or TFLOPS is often not enough to see the full picture. What truly matters is how much of that theoretical power translates into actual training efficiency.👇

If you want to understand AI, you need to look at the entire system, not just the model. 🔔 AI workloads rely on a layered technology stack that spans compute, data, software, and orchestration. Performance emerges from how these layers work together. #AIInfrastructure

Faster LLM training isn’t about more GPUs. At scale, training throughput is often synchronization-bound, not compute-bound. As model size and parallelism grow, collective operations (e.g. All-Reduce) amplify tail latency. One straggler can stall the entire cluster.

Modern inference optimization focuses less on raw compute: – KV cache quantization and compression (FP8, 4-bit) – Paged KV management to reduce fragmentation – Cache reuse and eviction for long-running sessions – Memory-aware scheduling with dynamic batching

Faster LLM inference isn’t about adding more GPUs.😎 In production, inference bottlenecks are often memory-bound, not compute-bound. As context length and concurrency grow, KV cache pressure saturates memory bandwidth long before GPUs hit peak utilization.

3 counter-intuitive ways to boost GPU utilization 🚀 Improving GPU utilization doesn't always need more hardware. 1. Tune Batch Sizes: Don't stick to defaults. 2. Embrace Mixed Precision: Use FP16/FP32. It’s faster, saves memory. 3. Co-Locate Compute: Latency kills throughput.

We hope you can join us as we kick off the year with a thoughtful conversation! 🔗 Registration is open here: scitix-ai.com/Beyond-the-Wai…

The future of LLM inference isn’t about GPU count. It’s about cost per token. As workloads become memory-bound, inference economics—not peak compute—define what can scale. That’s why Blackwell matters.