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Kimi K3 is now available on Runpod

Rent AMD MI350X GPUs from $5.49/hr

Data center accelerator built on AMD CDNA 4 architecture with HBM3E memory and 8 TB/s of bandwidth, for large-model inference and training where memory capacity per accelerator is the constraint.

Powering the next generation of AI & high-performance computing.

Engineered for large-scale AI training, deep learning, and high-performance workloads, delivering unprecedented compute power and efficiency.

AMD CDNA 4 Architecture

CDNA 4 matrix cores add micro-scaling OCP MX formats including MXFP8, MXFP6 and MXFP4, alongside FP16, BF16, FP8 and INT8.

HBM3E Memory

HBM3E on an 8192-bit interface with 8 TB/s of peak bandwidth and 256MB of Infinity Cache.

OAM Form Factor

OAM modules on a UBB8 baseboard, compatible with prior-generation MI325X platform designs.

ROCm Software Stack

Runs the ROCm stack, so PyTorch and vLLM workloads port across without CUDA.

Why rent the MI350X instead of buying?

The most memory per accelerator available here

Memory capacity per card is the usual reason to pick this over an NVIDIA part, letting a large model sit on one accelerator rather than being split across several.

Pay only for what you use

Runpod bills Pods per second with no minimum, so a short run costs what it costs rather than a full hour.

An AMD option without a sales call

Most clouds carrying CDNA hardware route it through enterprise sales. This deploys self-serve from the console.

Key specs at a glance.

Performance benchmarks that push AI, ML, and HPC workloads further.

Memory Capacity

294

GB HBM3E

Memory Bandwidth

8

TB/s

FP8 Performance (dense)

4.6

PFLOPS

Popular use cases.

Designed for demanding workloads. Learn if this GPU fits your needs.

Inference workload illustration

Inference

Serve inference for image, text, and audio generation at any scale.

Fine-tuning workload illustration

Fine-tuning

Train custom models on your specific datasets.

AI agents workload illustration

Agents

Build intelligent agent-based systems and workflows.

Compute-heavy workload illustration

Compute-heavy tasks

Run compute-heavy workloads like rendering and simulations.

Ready for your most demanding workloads.

Essential technical specifications to help you choose the right GPU for your workload.

Specification
Details
Great for...
Memory Capacity
294 GB HBM3E
Holding very large models on a single accelerator without tensor parallelism across cards.
Memory Bandwidth
8 TB/s
Feeding large weight matrices at speed, which is what decides throughput on memory-bound inference.
FP8 Performance (dense)
4.6 PFLOPS
Quantized large-model serving. AMD also publishes 9.2 PFLOPS with structured sparsity; this is the dense figure.
"The Runpod team has clearly prioritized the developer experience to create an elegant solution that enables individuals to rapidly develop custom AI apps or integrations while also paving the way for organizations to truly deliver on the promise of AI."

Amjad Masad

"Runpod is the only place I can deploy high-end GPU models instantly. No sales calls, no rate limits, no nonsense."

Daniel Chang

“The main value proposition for us was the flexibility Runpod offered. We were able to scale up effortlessly to meet the demand at launch.”

Josh Payne

“Runpod helped us scale the part of our platform that drives creation. That’s what fuels the rest. Image generation, sharing, remixing. It starts with training.”

Matty Shimura

Powerful GPUs. Globally available. Reliability you can trust.

30+ GPUs, 31 regions, instant scale. Fine-tune or go full Skynet. We’ve got you.

Community Cloud
$/hr
Secure Cloud
$5.49/hr
Unique GPU Models
Community Cloud
25
Secure Cloud
19
Global Regions
Community Cloud
17
Secure Cloud
14
Network Storage
Community Cloud
Secure Cloud
Enterprise-Grade Reliability
Community Cloud
Secure Cloud
Savings Plans
Community Cloud
Secure Cloud
24/7 Support
Community Cloud
Secure Cloud
Delightful Dev Experience
Community Cloud
Secure Cloud

12,380,000,000

Requests since launch & 1M+ developers worldwide

Build what’s next.

Build, train, and scale AI workloads on Runpod with cloud GPUs, Serverless, and Clusters.

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