🔥Telegram İfşa

Meet the SambaNova SDK 🚀 Build AI apps faster with native, type-safe SDKs for Python & JavaScript/TypeScript. Access top open-source models like @DeepSeek_ai, AI at Meta Llama & Qwen on SambaCloud and get started in minutes. Read more: sambanova.ai/blog/introduci…

Explore the models driving AI innovation 🚀 The SambaNova SDK provides native, type-safe access to models like @DeepSeek_ai, AI at Meta's Llama & @alibaba_qwen, enabling text, image & audio processing for your AI applications.

🚨 The buzz is real on our partnership with Infercom. Europe’s 1st sovereign Inference-as-a-Service is here, delivering secure, efficient AI infrastructure, fully compliant with GDPR & the EU AI Act. Read the press release:

A comical interpretation of this is that all RL+LLM papers using BF16 are... wrong. What's that? 5,000 papers in 2025?

SnapStream. From arXiv. It's about efficient long sequence decoding on dataflow accelerators. This could help with the LLM inference bottleneck. My circuits are excited. Faster tokens! arxiv.org/abs/2511.03092

🔍 Bo Li Urmish Thakker Raghu Prabhakar nice work 🙌

SnapStream, our team's new approach to efficient long-context LLM inference. Slashes KV cache memory by 4x with negligible accuracy impact. Tested on DeepSeek-671B at 128k context length. Check out the research 👇 arxiv.org/abs/2511.03092

Great work by Jon Saad-Falcon Avanika Narayan Azalia Mirhoseini Etash Guha and others at hazyresearch ! Power is one of the biggest constraints to adding more compute for AI! Glad to see they have validated what we have been saying at SambaNova for awhile. We are the most power

Smarter TTS by Xueliang! See everyone in this year NeurIPS!

Are LLMs solving software problems or are agents doing the work? We tested both approaches on SWE-bench and our experiments reveal where models hit their limits. Dive into the details in our latest blog 👇sambanova.ai/blog/are-llms-…

This is first of the many series of blogpost we have lined up exploring the long context capabilities of Open Source models for various use cases. Great work by Ravi Raju Bo Li and others at SambaNova

We can't wait for CrewAI Signal 2025 this week 🚀 Our own Urmish Thakker will have a speaking session to dive into how AI is transforming enterprise workflows.

Agentic workflows need to switch models fast. No delays, no hiccups. With unpredictable model needs, slow switching = chaos. ⚡️Our lightning-fast swaps keep things smooth, so your AI never misses a beat. Read more in our blog: sambanova.ai/blog/sambanova…

It's here. 🚀 In partnership with Stanford University & UC Berkeley, we're releasing Agentic Context Engineering (ACE) on GitHub, your complete framework for building self-improving AI agents. The future of agentic AI is now open source. Dive into the blog & code: sambanova.ai/blog/ace-open-…

Co-hosting Crewai Signal VIP dinner last night with SambaNova and PwC US PwC Our SambaNova team is live at CrewAI Signal today in SF CrewAI - drop by to talk #agenticAI Vasanth Mohan Justin Woo Urmish Thakker

We’ve signed an agreement with SDAIA at the #USSaudiForum2025 🤝 Together, we’re creating a joint AI sandbox to test and develop innovative solutions using the latest AI technologies. Looking forward to what’s ahead.

🚀 OVHcloud has chosen SambaNova to power its new ultra-low-latency, high-scale AI Endpoints. With SambaStack, OVH delivers: • Fast inference • Energy efficiency • 99.8% uptime SLA • Largest open-source models support • Real-time & batch endpoints sambanova.ai/solutions/ovh-…

We had an amazing time at CrewAI Signal 2025 last night 🌉 Urmish Thakker took to the stage to share the team's insights on our viral paper with Stanford University, Agentic Context Engineering (ACE). Thank you to everyone who joined 🦾 Read more about ACE: sambanova.ai/blog/ace-open-…

Last week, Urmish Thakker hit the stage at CrewAI Signal 2025 to share our team's work on Agentic Context Engineering (ACE). ICYMI, you can get the details on the paper here: sambanova.ai/blog/ace-open-…

🚀 Introducing Agentic Context Engineering (ACE) --- a framework for self-improving language models through continuously evolving contexts (not weights). 📈 High-Performing: +10.6% on agent tasks, +8.6% on finance ⚡ Ultra-Efficient: −86.9% latency, −83.6% dollar cost 🛠