Fireworks
@FireworksAI_HQ
The frontier platform for training and inference on open-weights models at scale.
280 Following 29.4K Followers
You can now fine-tune Kimi K3 on Fireworks. Conduct supervised fine-tuning, preference tuning, and reinforcement learning via Training API.
Run across dedicated, and serverless training. The first open frontier model at 3 trillion parameters, ready for your product.
Contact us to get started:
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Kimi K3 is live on Fireworks. Day 0, inference and training. US-hosted, and zero data retention.
This is the first frontier open model in the 3 trillion parameter class. It sports 1M context, native vision, and reasoning that rivals the top closed models.
Boom.
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We ran Kimi K3 against Fable on ~1,000 agentic tasks, expecting a catch-up story. We got a specialization story instead.
@kimi_moonshot's K3 outperformed on security, crypto, and long terminal loops. Fable beat on multi-lang + web/data viz. Per-task routing hits 93% accuracy, above BOTH models, at up to 50x lower cost than Fable on long loops.
The part nobody's pricing in yet: the router sends 72-96% of traffic to K3. The frontier model becomes the fallback rather than the default.
Kimi K3, coming to Fireworks July 27.
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Open-weight models like GLM-5.2 and Kimi K2.6 demonstrate the capability & performance needed for real GTM work.
@ClayRunHQ's team recognized that early, and we're proud to be the inference layer making it real.
Congrats to @jeffbarg and Clay. Let's keep building together.
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Specialized intelligence just became a $17.5 billion idea.
Last week @lqiao made the case on the RAISE Summit Master Stage. This week @FireworksAI_HQ announced a $1.5B Series D — $1B+ ARR, up 5x YoY, 40 trillion tokens served daily.
Her argument: efficiency means specializing the tool to its value. "We don't drive a Ferrari to grocery shopping."
Full session 👇
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It has been an incredible day, thank you all for celebrating with us.
If you'd like to come build with us, we can't wait to meet you:
This is only the beginning.
Thanks to all our incredible customers and partners for being part of this journey.
We’re entering an era of specialized intelligence, where every company will build models tailored to its users that continuously improve over time. It’s time to own your intelligence and control your destiny.
If you’re building the future of AI, we’d love to build with you.
The future of AI is specialized. We’re just getting started.
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Been tracking and meeting @lqiao
since the Series A with my partner @timt . finally, partnering up with @FireworksAI_HQ ! They have nailed the intersection of custom models, frontier intelligence, and lowest most efficient token delivery
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Grateful to @BessemerVP for being an early believer. The curve isn't really ours to claim. It belongs to every builder who choses to own their intelligence instead of renting it. We just built the platform to make it possible.
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𝐅𝐢𝐫𝐞𝐰𝐨𝐫𝐤𝐬 𝐠𝐫𝐞𝐰 𝐟𝐫𝐨𝐦 $𝟏𝟎𝟎𝐌 𝐀𝐑𝐑 𝐭𝐨 $𝟏𝐁 𝐀𝐑𝐑 𝐢𝐧 𝟏𝟔 𝐦𝐨𝐧𝐭𝐡𝐬!
@AnthropicAI set the revenue trajectory for the next generation of AI Giants. And @FireworksAI_HQ demonstrates the path to $1B ARR in 4 years, isn’t a fluke, but a new possibility.
𝐅𝐢𝐫𝐞𝐰𝐨𝐫𝐤𝐬 (𝐉𝐚𝐧 𝟐𝟎𝟐𝟐) 𝐯𝐬. 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜 (𝐉𝐚𝐧 𝟐𝟎𝟐𝟏)
— 𝐅𝐨𝐮𝐧𝐝𝐢𝐧𝐠 → $𝟏𝟎𝟎𝐌 𝐀𝐑𝐑: Both took ~3 years
— $𝟏𝟎𝟎𝐌 → $𝟏𝐁 𝐀𝐑𝐑: Fireworks (16 months) vs. Anthropic (11-12 months)
— 𝐅𝐨𝐮𝐧𝐝𝐢𝐧𝐠 → $𝟏𝐁 𝐀𝐑𝐑: Fireworks (~4 years) vs. Anthropic (~4 years)
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Fireworks AI says it raised $1.5B at a $17.5B valuation and is already at $1B ARR.
@natolambert's read: inference companies with real revenue may turn into AI labs faster than some research-first labs become real businesses. Digg is tracking 62.6K views.
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Our Fine-tuning 101 webinar is just about to start, you still have time to jump in:
Can't make it this time? That's alright, we'll post the webinar on YouTube this week!
Our free monthly DevRel webinar series kicks off this Thursday, 10am PST.
Fine-tune an open vision model to extract clean, structured JSON from messy receipt images, managed start to finish on Fireworks.
Plus recent releases and a fine-tuning primer.
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Every company must own its intelligence.
We've raised $1.5B Series D at a $17.5B valuation.
We’ve surpassed $1B ARR and serve over 40 trillion tokens daily, with 95%+ coming from models specialized on customer data.
We’re just getting started.
More:
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Generic AI models are not built for medicine.
Doximity Ask, trained and served on Fireworks, outperformed GPT-5.6 Sol, Claude Fable 5, and OpenEvidence in a Stanford-Harvard clinical AI safety study.
Specialized intelligence beats the generic version when safety matters. @doximity proved it.
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Finally: an independent, head-to-head test of clinical AI models.
This week, Stanford's Arise Lab published one of the most comprehensive independent evaluations of clinical AI to date. The benchmark compared 24 frontier and clinical AI models, producing 12,747 expert rankings on their answers to real-world clinical questions.
We were excited to see Doximity Ask outperform
OpenEvidence, GPT-5.6, Claude Fable 5, Gemini 3.1 Pro, and other leading frontier AI models.
The bigger story, though, is that clinical AI is finally getting the kind of rigorous, independent benchmarking it needs.
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The best base model for training isn't about someone else's benchmarks.
Excited to offer @thinkymachines' first open-weights model: Inkling. 975B MoE, multimodal, with Apache 2.0. A great foundation for fine-tuning. Available day-zero.
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Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
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Trending AND fastest-growing in the same month? Benchmarks change daily, but only @tryramp has the database of real receipts to track this. We can confirm: demand for open-weight inference and training is on fire🔥. Congrats to @HiggsField_AI landing on both lists too.
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New: Top SaaS Vendors on @tryramp July 2026
Two trends this month:
1. AI support bots are one of the clearest enterprise AI use cases showing up in payments data (PolyAI, Sierra)
2. More and more usage of open source and Chinese models (DeepSeek, Fireworks AI, Baseten, Together, OpenRouter)
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He's right, you know.
Turns out you can get an opus 4.8 level model perf at 190 tokens per second and 1/4th of the price if you use an Open weights model called GLM-5.2 Fast.
Last month we hosted an AI Nerd Meetup @Workato HQ featuring builders tackling the hardest problems in agentic AI.
Highlights:
@yuan_sihan of Fireworks - how open-source agents can use a frontier model as a reviewer to hit much higher quality without paying frontier costs at every step.
The research behind our latest post, "Frontier AI at a fraction of the cost":
@KovacVeljko of @cognee_ - fact disambiguation and agent memory: how agents retain, retrieve, and use info over time, and why open-source memory infra matters in production.
@mat_pichler of @wordware - collaborative filesystems for agents: how shared file and workspace layers help agents coordinate, persist work, and collaborate.
Thanks to our speakers and Workato for hosting!
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Our free monthly DevRel webinar series kicks off this Thursday, 10am PST.
Fine-tune an open vision model to extract clean, structured JSON from messy receipt images, managed start to finish on Fireworks.
Plus recent releases and a fine-tuning primer.
Show more
ICYMI: @LangChain Deep Agents on @nvidia Nemotron 3 Ultra. frontier open-model agents at ~10x lower cost than closed.
Run on Fireworks, then post-train it into specialized intelligence you own.
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