Kimi K3 on AWS: HyperPod vs EKS for Production AI Agents
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A 2.8-trillion-parameter model served on eight B300 GPUs changes the deployment conversation. Kimi K3 on AWS is technically mapped out; the practitioner problem is choosing how much infrastructure your team should own. AWS documents two production routes: SageMaker HyperPod and plain Amazon EKS. Both use vLLM as the serving engine and MXFP4 as the quantization format, but they leave different amounts of platform work with your team. Start with the workload, not the cluster Kimi K3 has about 104…
1Key Takeaways
- A 2.8-trillion-parameter model served on eight B300 GPUs changes the deployment conversation.
- Kimi K3 on AWS is technically mapped out; the practitioner problem is choosing how much infrastructure your team should own.
- AWS documents two production routes: SageMaker HyperPod and plain Amazon EKS.
- Both use vLLM as the serving engine and MXFP4 as the quantization format, but they leave different amounts of platform work with your team.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that a 2.8-trillion-parameter model served on eight B300 GPUs changes the deployment conversation.
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