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Fireworks Inference

The highest performance inference for your specialized intelligence.

Serve frontier open models, or your own post-trained versions of them, on an engine optimized at every layer.

NVIDIA GTC 2026, Jensen Huang

The infrastructure layer for continuously improving specialized intelligence.

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Every layer optimized for production

Best performing inference stack

A fully disaggregated inference engine optimized from custom kernels to memory management. Deliver up to 4x higher throughput and industry-leading latency without sacrificing model quality.

Built for any scale

From solo developers to Cursor-scale workloads. Long prompts, agent loops, and multi-turn conversations run fast and reliably, even as traffic, context length, and concurrency grow.

The latest models

Access to hundreds of models and the latest open-source frontier releases as soon as they become available.

Three ways to run

Pick how you want to serve

Serverless

Call a model with one request, pay per token. OpenAI and Anthropic compatible, so you can migrate by swapping a URL.

On-Demand

Dedicated deployments for serving post-trained specialized models with support for multi-region deployments and custom performance optimizations.

Reserved Capacity

Get guaranteed capacity, higher quotas, early access to the newest regions and hardware, and more. Supports increased deployment flexibility with BYOC.

When performance is the product

A disaggregated engine, optimized end to end

Most providers run a general-purpose serving stack and tune at the edges. We built it from the ground up, optimized every layer we control, from GPU memory layout to the runtime, and disaggregated every stage so each one scales on its own.

Disaggregated prefill and decode

Prefill and decode have different hardware profiles. We split them onto separate pools so each scales independently, cutting latency without stranding GPUs.

Optimizations from top to bottom

Inference optimizations including custom kernels, precision and quantization innovations, speculative decoding, and advanced caching strategies.

KV cache and routing for long context

Disaggregated KV caching and prompt-aware routing keep long prompts and multi-turn sessions fast.

Multi-node expert parallelism

Frontier MoE models served across nodes with composable parallelism, so trillion-parameter models run at production speed.

Serve the intelligence you build

The model you train on Fireworks serves on the same stack, with the same kernels and quantization. What you train is what you serve, with no handoff and no migration. Inference and training feed each other in one loop.

Leading AI companies serve on Fireworks

Cresta

"Fireworks' Multi-LoRA capabilities align with Cresta's strategy to deploy custom AI through fine-tuning cutting-edge base models. It helps unleash the potential of AI on private enterprise data."

Tim Shi
Tim Shi | Co-Founder at Cresta
Motif

“Using Fireworks AI on Foundry, we can run repeatable, high-volume evaluations through a single Azure endpoint, which helps our team move faster from deployment to informed model decisions with more confidence.”

Hanbin Jung Headshot
Hanbin Jung | Partnership Lead at Motif
Cursor logo dark
why did Cursor rollout Composer 2 with @FireworksAI_HQ?

"...because it's way more performant than the open source engines and is what we use in production. our rl inference scales elastically and globally because of it. when we have low prod traffic we scale up RL, when we have high prod traffic, we scale down RL."

federico cassano
Federico Cassano | AI Researcher at Cursor
Vercel Dark

"Vercel’s v0 model is a composite model. The SOTA in this space changes every day, so you don’t want to tie yourself to a single model. Using a fine-tuned reinforcement learning model with Fireworks, we perform substantially better than SOTA."

Malte Ubl, CTO at Vercel
Malte Ubl | CTO at Vercel
Notion logo dark

"By partnering with Fireworks to fine-tune models, we reduced latency from about 2 seconds to 350 milliseconds, significantly improving performance and enabling us to launch AI features at scale. That improvement is a game changer for delivering reliable, enterprise-scale AI."

Sarah Sachs
Sarah Sachs | AI Lead at Notion
genspark

"Fireworks enabled us to own our AI journey, and unlock better quality in just four weeks."

Kay Zhu
Kay Zhu | CTO at Genspark
Quora

"We've had a really great experience working with Fireworks to host open source models, including SDXL, Llama, and Mistral. After migrating one of our models, we noticed a 3x speedup in response time, which made our app feel much more responsive and boosted our engagement metrics."

SPENCER CHAN
Spencer Chan | Product Lead at Quora
Sourcegraph

"Fireworks has been a fantastic partner in building AI dev tools at Sourcegraph. Their fast, reliable model inference lets us focus on fine-tuning, AI-powered code search, and deep code context, making Cody the best AI coding assistant. They are responsive and ship at an amazing pace."

Beyang Liu Testimonial
Beyang Liu | CTO at Sourcegraph
Ui Path

By running Fireworks AI on Azure Foundry, UiPath powers both Autopilot and Delegate with open models that are significantly faster and more cost-efficient for Computer Use, all while matching the quality of Claude's Sonnet 4.6. It's a step-change in how we deliver AI at scale to our customers.

Neagovici-Negoescu
Mircea Neagovici-Negoescu | SVP, Head of AI at UiPath
Cursor logo dark

“Fireworks has been an amazing partner getting our Fast Apply and Copilot++ models running performantly. They exceeded other competitors we reviewed on performance. After testing their quantized model quality for our use cases, we have found minimal degradation. Fireworks helps implement task specific speed ups and new architectures, allowing us to achieve bleeding edge performance!”

Sualeh Asif Testimonial
Sualeh Asif | CPO at Cursor
genspark

"Fireworks enabled us to own our AI journey, and unlock better quality in just four weeks. This resulted in a better user experience for our customers."

Kay Zhu
Kay Zhu | CTO at Genspark
rLLM

"The rLLM team is dedicated to pushing the boundaries of autonomous AI, which means our time is best spent on innovation rather than managing backend clusters. The Fireworks Training SDK lets us focus on our research instead of wrestling with infrastructure. The platform is fast, well-optimized, and just works."

rLLM
Kyle Montgomery & Sijun Tan | Core Contributors, rLLM at rLLM
Cresta

"Fireworks' Multi-LoRA capabilities align with Cresta's strategy to deploy custom AI through fine-tuning cutting-edge base models. It helps unleash the potential of AI on private enterprise data."

Tim Shi
Tim Shi | Co-Founder at Cresta
Motif

“Using Fireworks AI on Foundry, we can run repeatable, high-volume evaluations through a single Azure endpoint, which helps our team move faster from deployment to informed model decisions with more confidence.”

Hanbin Jung Headshot
Hanbin Jung | Partnership Lead at Motif
Cursor logo dark
why did Cursor rollout Composer 2 with @FireworksAI_HQ?

"...because it's way more performant than the open source engines and is what we use in production. our rl inference scales elastically and globally because of it. when we have low prod traffic we scale up RL, when we have high prod traffic, we scale down RL."

federico cassano
Federico Cassano | AI Researcher at Cursor
Vercel Dark

"Vercel’s v0 model is a composite model. The SOTA in this space changes every day, so you don’t want to tie yourself to a single model. Using a fine-tuned reinforcement learning model with Fireworks, we perform substantially better than SOTA."

Malte Ubl, CTO at Vercel
Malte Ubl | CTO at Vercel

FAQ

Is Fireworks OpenAI and Anthropic compatible?

Yes. Fireworks is compatible with both, making it easy to migrate applications that use OpenAI or Anthropic’s Messages API.

How fast do new models become available?

As an official launch partner for all major model providers, we often provide day zero support for major launches.

What models and modalities are supported?

Qwen, Kimi, DeepSeek, MiniMax, Nemotron, GLM, Llama, and many more across text and vision language models. The full list of 250+ models is in the model library.

How do you handle uptime and security?

Production-grade reliability with autoscaling across regions. SOC 2 Type II, HIPAA-ready, and GDPR-compliant. We never use your inference inputs or outputs for any other purpose. Full security posture is in the docs.

Serve your model on Fireworks

Start serverless in minutes, or talk to our team about on-demand and reserved capacity.