Fast ML inference. Run top AI models using a simple API.

Palo Alto
Joined February 2023
Demand for DeepSeek V4.1 Flash has been nuts. People have agents grinding on it all weekend, nonstop. So screw it, we're cutting the price 51% 🚀🚀. keep them running.
DeepSeek keeps shipping while the rest of the timezone sleeps, so we stopped sleeping too 😈. V4.1-Flash-1017 is live on DeepInfra: hosted in the US, zero data retention, same price as V4.1-Flash with 30% off. Link in the comment ⬇️
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DeepSeek keeps shipping while the rest of the timezone sleeps, so we stopped sleeping too 😈. V4.1-Flash-1017 is live on DeepInfra: hosted in the US, zero data retention, same price as V4.1-Flash with 30% off. Link in the comment ⬇️
🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4
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Proud to sponsor SGLang Summit. We'll see you there!
Proud to welcome our newest sponsors to SGLang Summit. In just one week since our public announcement, our sponsor lineup has grown from 11 to 36. Thank you to every sponsor helping bring this community together. Join 2,000+ engineers, researchers, and builders from across the AI ecosystem for technical sessions, hands-on workshops, and the GPU Expo. Hear from Lip-Bu Tan, Lilian Weng, Soumith Chintala, Aravind Srinivas, and more. Meet the teams building across AI hardware, infrastructure, models, and applications. 🗓️ November 12–13, 2026 🌉 Fort Mason, San Francisco 🎟️ Get your tickets: sglang.io/summit
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DeepInfra retweeted
Glimpse is our first model, built for visual thinking Try it for yourself platform.elorian.ai/
Spent years watching models ace a single image and completely whiff on anything that moves or changes angle. Today we’re fixing that. Try Glimpse now, our first visual thinking model with competitive frontier performance compared to Astra on key public visual reasoning benchmarks.
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Big congrats to @ElorianAI on the launch of Glimpse. Visual reasoning is one of the most exciting frontiers in AI, great to see the team pushing it forward. Check it out 👇
Spent years watching models ace a single image and completely whiff on anything that moves or changes angle. Today we’re fixing that. Try Glimpse now, our first visual thinking model with competitive frontier performance compared to Astra on key public visual reasoning benchmarks.
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DeepInfra retweeted
Today we're thrilled to announce the release of our first frontier visual reasoning model
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Step 5 Preview from @StepFun_ai is live on DeepInfra, day zero. 600B open-weight MoE, 27B active. 1M context, text + vision. FP8. $1.00 in / $2.70 out / $0.05 cached per 1M tokens. Built for long-horizon agents: coding, frontend, finance. What would you load into 1M tokens first?
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Up to 626 output tokens/s at 100K input, measured by @ArtificialAnlys on DeepInfra. NVIDIA Nemotron 3.5 Super VL from @nvidia is live! Build agents that read documents, screenshots and video, reason about what they see, and generate tool calls. 120B-A12B MoE. OpenAI-compatible API. Pay per token. $0.20 in / $1.00 out | $0.10 cached per 1M Try it → deepinfra.com/nvidia/NVIDIA-…
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Day-zero drop: Ling-3.1-flash from @AntLingAGI is now live on DeepInfra ⚡ Built for long-horizon coding, multi-step analysis, and tool-using agents with 560B total parameters and 25B active per token. Per 1M tokens: $0.30 input · $0.90 output · $0.06 cached. Try it, compare it, and tell us what you build.
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DeepInfra retweeted
I make this same post like every 2-3 years, because there's always new data further supporting it. Georges Harik (@gharik) is one of the most prescient people in AI, and definitely the least well known and underappreciated one. In 1999 he joined Google as one of the first employees (like #7 or something) because he thought he could use his AI knowledge to build a better ad system. He and Salar Kamangar built Adwords (Salar later bought YouTube and Georges later was the force behind Google wanting to buy Andy rubin's company to build Android) Georges left Google so he could focus on AI research, which he self funded. He did so with someone he worked with closely at Google, our friend Noam Shazeer (they built the AdSense targeting engine). When Google finally decided to work hard on AI around the time of the deepmind acquisition, they tried to get Georges and Noam to come back. Georges wanted to stay independent, and our friend Noam went on to do great things obviously. This was my good fortune because it led to Georges and I hanging out a lot, because he basically needed a new lunch buddy. Georges at the time was working on language models and despite karpathy saying nobody saw LLMs being so big even in 2017, Georges was telling me they were on the cusp of changing everything in 2013. My wife and I literally changed some of our parenting priorities that year (less tigery) because georges convinced me the world was going to change so much. Later on he and I got involved in a company making CV photo filters called Lambda. They needed to make money to survive so they started building ML boxes which Georges bought a bunch of. But his significant other was mad there were so many loud and hot boxes in the house, she started worrying about fire risk, so Georges told the lambda guys they should build a cloud because in the future, the power density of the systems was going to be too big for even most enterprises to handle. This was like 2017. That's how lambda cloud began, and the premise Georges had was that AI was going to change things so much, demand growth would outpace supply growth, and the value of compute was eventually going to rise and that lambda was effectively going to be a REIT... so we should own as many GPUs as possible. It took the world almost a decade to finally figure out what Georges saw then (see attached tweet). About 5 years ago when pretraining was still the only thing people cared about, Georges started telling me inference was going to be super impt. He was working on research investigating post training as a way to improve reasoning. He found some other guys working on the same idea that had a paper known as the STAR paper, and they worked together to prove it even further with another paper called quietstar. (They all later cofounded the neolab humans&). Georges got some friends to build deepinfra.com to be one of the first inference clouds because he saw it all early. He and the humans& team also smartly used all their money to buy compute knowing it would go up in price and also get harder to obtain. It's kind of amazing to me how he has gotten everything right so far ahead of time. It's also amazing to me how VCs don't really get or believe a lot of the things he says, they often tell me I am wrong whenever I parrot Georges' current thoughts... and then years later they all put out charts and papers reaffirming those same things as if they knew it all along. The founders of Google recognize Georges' brilliance and still talk to him a lot. (Besides being smart, he is a lot of fun to hang out with.) I'm just wondering when the rest of the world figures it out
I can't emphasize enough how important this @a16z chart seems and how few have internalized it.
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DeepInfra retweeted
Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. nvda.ws/4hcoq7m
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Agents need more than model-level guardrails. They also need runtime boundaries that control what they can access and do. For the NVIDIA Open Agent Safety Platform launch, we’re sharing how DeepInfra approaches agent isolation, our work with OpenShell, and a tested guide for using DeepInfra inference inside an OpenShell sandbox. Read more: deepinfra.com/blog/agent-run…
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DeepInfra retweeted
I used my entire usage limit for BOTH Codex and Claude Code (2 accounts) to tackle a local build, and the problem was never solved even though I used Fable 5.1 for intensive planning, parallel-agent supervision, and evals. I then went to @DeepInfra and got an API key for DeepSeek V4.1-Flash (US-hosted), and it is now CRUSHING this massive task Both Fable 5.1 & Astra could not. 50% of the way through & I only spent $1.15 so far. This is why all of a sudden the AI Cartel is sounding the alarm about AI safety. It's not about safety, it's about caping the competition. The numbers speak louder than perceived good will of the Effective Altruism mafia.
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Encoder-decoder is back 😈!!! In DeepSeek V4.1 Flash the first 20 layers build the global KV that the next 20 read from, so prefill costs about half. Rolled out over the last 24h: throughput doubled, and we are approaching 1T tokens/day on OpenRouter. 30% off to celebrate. Enjoy. Cheapest on the market, as always.
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