Your model, any compute, one platform. Run AI across GPUs and CPUs - engineered for the most demanding inference workloads, from kernel to cloud.
2x Performance on a unified stack
Our unified infrastructure optimizes your AI pipeline with full-stack optimizations across text, image and video.
AI on any GPU
Same model, same codebase - seamlessly runs across NVIDIA, AMD, Intel, ARM, and Apple Silicon. True hardware portability.
50% cost saving
Higher GPU utilization, faster model compilation & runtime, and dynamic hardware selection means savings compound at scale.
Most AI infrastructure today is assembled from parts that were never designed to work together: one tool for serving, another for optimization, another for custom kernels, something else for scaling. Every layer you add is another place where things break.
We built Modular to fix that.
One unified stack from kernels to cloud, built from the ground up for heterogeneous compute. Scale AI from cloud to edge - CPUs, GPUs, and ASICs.
Run AI workloads at production scale with SOTA performance on NVIDIA and AMD GPUs in Modular’s hosted cloud or your VPC. Our full-stack approach enables complete workload customization, performance tuning, and deep observability.
Our high-performance, hardware-agnostic serving framework, MAX, automatically optimizes kernels and request execution across accelerators. 2x performance improvement over vLLM on diverse hardware through a single container and OpenAI-compatible API.
Run 1000+ models like DeepSeek and Kimi out of the box with MAX. PyTorch-like model APIs and AI coding skills make it easy to port custom models in minutes.
100s of SOTA, composable kernels written in our high-performance systems language, Mojo. Extend or write custom GPU kernels for maximum performance across accelerators.
Modular was built to be natively heterogeneous. Run workloads seamlessly across NVIDIA, AMD, and Apple GPUs as well as Intel, AMD, and ARM CPUs.
High-performance inference. No infrastructure to manage, no long-term commitments. Test easily. Per-token pricing.
Reserved NVIDIA and AMD GPUs. Per-minute pricing that’s easy and flexible.
Bring your own custom or fine-tuned models. Deploy on optimized infrastructure with per-minute pricing.
from openai import OpenAI
client = OpenAI(
base_url="https://deepseek-v31.{org_name}.api.modular.com/v1",
api_key="MODULAR_API_KEY",
)
completion = client.chat.completions.create(
model="deepseek/deepseek-chat-v3.1",
messages=[
{
"role": "user",
"content": "Who won the world series in 2020?"
},
],
)
print(completion.choices[0].message.content)
Fully managed by Modular + we handle SLAs and security so you don't have to. Best token economics on NVIDIA and AMD.
Your Cloud with diverse hardware support. Modular manages the control plane while inference runs in your VPC. You own the hardware, data, and cloud credits.
MAX and Mojo in a container, running on your infrastructure. NVIDIA, AMD & more - your hardware, your cloud, your rules.
Faster Time to first audio
“Our collaboration with Modular is a glimpse into the future of accessible AI infrastructure.”
Time to first token (TTFT)
Keep every conversation instant. MAX delivers sub-second mean time to first token (TTFT. Patients get responsive, natural interactions with no perceptible delay.
Build with popular models
Frontier-class models (V3, R1) built for complex reasoning, coding, and math — at dramatically lower inference cost than comparable proprietary models.
Moonshot AI's 1T parameter MoE model optimized for agentic tasks, tool use, and coding.
Large-scale MoE model (456B params) optimized for long-context tasks up to 1M tokens.
Your models, your kernels, any hardware. Write once in MAX and deploy across GPUs & CPUs with no vendor lock-in.
Build by specific use case
AI copilots, automated refactoring, test generation, and production-ready code synthesis.
Text-to-image creation, creative assets, design prototyping, and visual content workflows.
Natural voice synthesis, multilingual narration, real-time speech, and audio generation
Run faster AI agents anywhere with compiler-optimized inference across NVIDIA, AMD, and Apple Silicon.
Write once, deploy everywhere
Breakthrough compiler technology that automatically generates optimized kernels for any hardware target.
Vendor Independence
Break free from GPU vendor lock-in. Modular delivers peak performance across NVIDIA and AMD.
Competitor Endpoints
(Other providers)
Easy to deploy
Fast setup and managed infrastructure
but...
Limited control
Generic optimizations
Vendor lock-in
NVIDIA Only
Self-Hosted Endpoints
(Other providers)
Maximum control
Custom kernels, full visibility, your hardware
but...
Significant operational overhead
Long setup and tuning cycles
You’re on your own
Modular eliminates the tradeoff, providing the simplicity of managed inference with engineering-level control.
Dedicated endpoints with predictable performance
Forward-deployed engineers optimizing your workloads
Compiler-level optimizations that fuse the entire inference graph
Custom kernel programmability in Mojo & Python
GPU portability across NVIDIA and AMD without rewriting code
Request a demo
No black boxes. No vendor lock-in. No operational burden.
Schedule a demo of Modular and explore a custom end-to-end deployment built around your models, hardware, and performance goals.
Distributed, large-scale online inference endpoints
Highest-performance to maximize ROI and latency
Deploy in Modular cloud or your cloud
View all features with a custom demo
Book a demo for a personalized walkthrough of Modular in your environment. Learn how teams use it to simplify systems and tune performance at scale.
Custom 30 min walkthrough of our platform
Cover specific model or deployment needs
Flexible pricing to fit your specific needs
Book a demo
Talk with our sales lead Jay!
Run any open source model in 5 minutes, then benchmark it. Scale it to millions yourself (for free!).
Install Mojo and get up and running in minutes. A simple install, familiar tooling, and clear docs make it easy to start writing code immediately.
“Mojo can replace the C programs too. It works across the stack. It’s not glue code. It’s the whole ecosystem.”
“I'm excited, you're excited, everyone is excited to see what's new in Mojo and MAX and the amazing achievements of the team at Modular.”
“The Community is incredible and so supportive. It’s awesome to be part of.”
“I tried MAX builds last night, impressive indeed. I couldn't believe what I was seeing... performance is insane.”
“I'm very excited to see this coming together and what it represents, not just for MAX, but my hope for what it could also mean for the broader ecosystem that mojo could interact with.”
“Tired of the two language problem. I have one foot in the ML world and one foot in the geospatial world, and both struggle with the 'two-language' problem. Having Mojo - as one language all the way through is be awesome.”
“Max installation on Mac M2 and running llama3 in (q6_k and q4_k) was a breeze! Thank you Modular team!”
"C is known for being as fast as assembly, but when we implemented the same logic on Mojo and used some of the out-of-the-box features, it showed a huge increase in performance... It was amazing."
“It’s fast which is awesome. And it’s easy. It’s not CUDA programming...easy to optimize.”
“Mojo and the MAX Graph API are the surest bet for longterm multi-arch future-substrate NN compilation”
“The more I benchmark, the more impressed I am with the MAX Engine.”
“A few weeks ago, I started learning Mojo 🔥 and MAX. Mojo has the potential to take over AI development. It's Python++. Simple to learn, and extremely fast.”
Mojo destroys Python in speed. 12x faster without even trying. The future is bright!
"It worked like a charm, with impressive speed. Now my version is about twice as fast as Julia's (7 ms vs. 12 ms for a 10 million vector; 7 ms on the playground. I guess on my computer, it might be even faster). Amazing."
"This is about unlocking freedom for devs like me, no more vendor traps or rewrites, just pure iteration power. As someone working on challenging ML problems, this is a big thing."
"Mojo gives me the feeling of superpowers. I did not expect it to outperform a well-known solution like llama.cpp."
“What @modular is doing with Mojo and the MaxPlatform is a completely different ballgame.”
"after wrestling with CUDA drivers for years, it felt surprisingly… smooth. No, really: for once I wasn’t battling obscure libstdc++ errors at midnight or re-compiling kernels to coax out speed. Instead, I got a peek at writing almost-Pythonic code that compiles down to something that actually flies on the GPU."
“Mojo destroys Python in speed. 12x faster without even trying. The future is bright!”
"Mojo is Python++. It will be, when complete, a strict superset of the Python language. But it also has additional functionality so we can write high performance code that takes advantage of modern accelerators."
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。
