ScitiX

Shaping the Future with Intelligent Computing!

[ScitiX @scitix]

Helix (SparseRL) is now OPEN-SOURCE!

Training massive models like DeepSeek-V3? Moving 1.3TB of weights (671GB×2 in BF16) between the RL trainer and rollout can add >2 minutes per step just on data transfer.

[ScitiX @scitix]

Everyone talks about compute. But for large-scale AI systems, weight movement is quietly becoming just as critical a bottleneck.

At ScitiX, we've spent months attacking this problem — not with faster GPUs, but with system-level optimization across the entire weight loading pipeline.

Built on Supernode C2C + NVSwitch with Memory Pooling, GSCP, Multicast, and end-to-end orchestration, we've developed two core capabilities:
· FlashLoad — local weight loading, dramatically accelerated
DeepSeek V3-pro: 45s → 1.47s (31× faster)
DeepSeek V3-flash: ~14s → 0.34s (up to 42× faster)
· FlashClone — seed-node based distributed weight cloning across 8 nodes over NVSwitch
110s → 13.5s (8× faster)

The most surprising finding: the actual weight transfer itself only takes ~0.37s. The remaining ~13s is NCCL connection setup overhead. The bottleneck is no longer bandwidth — it's coordination and initialization.

Our next milestone: 110s → ~0.5s. Weight synchronization that's nearly invisible at cluster scale.
This matters more than ever for RL training, elastic inference, fast failover, dynamic scheduling, and large-scale agent workloads. As models scale, infrastructure efficiency will increasingly be defined by how fast systems can load, clone, and sync weights.

More details coming soon.

[ScitiX @scitix]

🔬 Ever wonder WHY large-model RL fine-tuning creates such sparse weight updates?
It’s not magic; it’s the "Three-Gate Theory." The ~99% sparsity is a structural consequence of how RL fundamentally operates under the hood:

Gate I (KL Anchor): RL uses small learning rates and KL constraints, tightly bounding the step magnitude.

Gate II (Model Geometry): The pretrained spectrum steers these bounded updates away from principal directions onto off-principal, low-curvature coordinates.

Gate III (BF16 Precision): The final cast from FP32 to BF16 acts as a filter, suppressing these tiny sub-threshold updates.
Together, these three gates explain the extreme sparsity we exploit in SparseRL-sync. We built the infrastructure so you can build the future.

Read our tech report to dive deeper: https://t.co/UtrGP8OOnM

[ScitiX @scitix]

🧠 Did you know? In large-model RL training, over 99% of your weight updates might be completely invisible to the inference engine.

Let’s look at the "Precision-Gated Gap" we discovered while building SparseRL-sync:
• On the Trainer side, the FP32 master weights are almost constantly changing (over 99.4% of elements update every step).
• But when cast to BF16 for Rollout, the changed elements drop below 1%!

Why? Most RL updates are micro-changes that fall below the BF16 quantization threshold. ()They get fully absorbed during the FP32-to-BF16 cast and vanish after rounding.

SparseRL-sync capitalizes on this exact phenomenon, slashing redundant data transfers by up to 100x. Stop sending data that doesn't exist!

[ScitiX @scitix]

🌍 The future of scalable RL is cross-cluster.

Since rollout (offline inference) is typically the bottleneck in RL, SparseRL-sync makes cross-region training a reality.

Typical Deployment:
Cluster A: Training & partial rollout
Clusters B & C: Pure rollout

Fully utilize nation-wide GPU resources without the data-moving headache. Build the next-gen AI with us at Scitix! 👇
🔗 https://t.co/UtrGP8OOnM

[ScitiX @scitix]

🔬 Built for extreme scale and versatility, Helix is battle-tested:

• Scale: Validated on models from 8B dense to massive 671B MoE.
• Algorithms: Fully covers the most popular RL methods—GRPO, DAPO, and GSPO.
• Flexibility: Supports both generic & agentic training.
• Ecosystem: Seamlessly compatible with Slime & Miles.

Say goodbye to network bottlenecks without sacrificing a single drop of model performance.

[ScitiX @scitix]

🚀 Helix (SparseRL) is now OPEN-SOURCE!

Training massive models like DeepSeek-V3? Moving 1.3TB of weights (671GB×2 in BF16) between the RL trainer and rollout can add >2 minutes per step just on data transfer.

Enter SparseRL-sync from Scitix: An efficient framework that slashes data movement by 30x ~ 100x with exactly 100% accuracy.

Code & Tech Report: https://t.co/UtrGP8OOnM

#ReinforcementLearning #OpenSource #AIInfrastructure #Scitix

[ScitiX @scitix]

🥂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

[ScitiX @scitix]

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!
🔗https://t.co/Ze22vXlLK2

#LLMs #OpenSource #AI #MachineLearning #KVCache

[ScitiX @scitix]

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

[ScitiX @scitix]

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! 🍻

[LMSYS Org @lmsysorg]

🍝🍷 Taking a break from the GTC rush? Join us for an exclusive Happy Hour.

SGLang × @radixark are hosting a GTC 2026 Happy Hour with friends across the AI infra ecosystem! Expect great food, unlimited drinks, and conversations with researchers & engineers from OpenAI, xAI, DeepMind, Meta, NVIDIA, Ollama, and top AI startups.

📅 Mar 17, 6:30–8:30 PM
📍 Walking distance from GTC (location shared after approval)

Last year’s GTC meetup was the first time we met the SGLang community in person. Since then, we’ve been shipping nonstop: LLM inference breakthroughs, RL infra, diffusion framework, and more. Let’s push AI infrastructure forward together.⚡
🎟 Register: https://t.co/j9sPmU9AZ2
🔔 Subscribe to our meetup calendar so you don’t miss future events: https://t.co/LbyWoPgVoO

With appreciation to our partners:
@radixark @ZZZZZPotentials @scitix
Looking forward to seeing many friends there. ✨

[ScitiX @scitix]

🛠️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: 👇
https://t.co/XK0KZJ08pn

[ScitiX @scitix]

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:
https://t.co/gJaVnDY0ol

[ScitiX @scitix]

🚀 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: https://t.co/QiLZ5dqeTu
💻 GitHub: https://t.co/9B6CWmY58d

#AIOps #DevOps #OpenSource

[ScitiX @scitix]

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.👇