GPU Management: Why Idle GPUs Are the New Grounded Aircraft

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The next frontier is managing an entire fleet of agents and there is nothing stopping it.</p>\n","updatedAt":"2026-07-30T17:54:18.152Z","author":{"_id":"69efeb839255a6a81398f6af","avatarUrl":"/avatars/31cf43fab2e1aaa5c4a6aa2310c8e193.svg","fullname":"Suranto","name":"Mikeleton","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9737027287483215},"editors":["Mikeleton"],"editorAvatarUrls":["/avatars/31cf43fab2e1aaa5c4a6aa2310c8e193.svg"],"reactions":[],"isReport":false},"replies":[{"id":"6a6d0a73f2f02aadd53c048e","author":{"_id":"67b22efa9b36260fe286e99f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/67b22efa9b36260fe286e99f/vm2fyO1NSqmjcIuyeF6PQ.jpeg","fullname":"Gabriel Pimenta de Freitas Cardoso","name":"GabrielPimenta99","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":22,"isUserFollowing":false,"primaryOrg":{"avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/67b22efa9b36260fe286e99f/5XWR_0JJv0gy6PRN9WDMF.png","fullname":"Dharma-AI","name":"Dharma-AI","type":"org","isHf":false,"details":"Specialized Small Language Models; Optimized Fine-Tuning; Efficient Inference","plan":"team"}},"createdAt":"2026-07-31T20:49:55.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"Agreed. And the part that isn't priced in yet: a fleet of agents makes per-token cost scale linearly with the business. At some point large enterprises stop paying that curve and bring the models in-house — which doesn't remove the cost, it just trades a vendor price list for a utilization number. And agent fleets are brutal on utilization: real-time inference under latency SLAs, batch jobs, retraining, evals — all bursty, all fighting over the same finite pool. Managing the fleet is going to be, in large part, a GPU scheduling problem.","html":"<p>Agreed. And the part that isn't priced in yet: a fleet of agents makes per-token cost scale linearly with the business. At some point large enterprises stop paying that curve and bring the models in-house — which doesn't remove the cost, it just trades a vendor price list for a utilization number. And agent fleets are brutal on utilization: real-time inference under latency SLAs, batch jobs, retraining, evals — all bursty, all fighting over the same finite pool. Managing the fleet is going to be, in large part, a GPU scheduling problem.</p>\n","updatedAt":"2026-07-31T20:49:55.496Z","author":{"_id":"67b22efa9b36260fe286e99f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/67b22efa9b36260fe286e99f/vm2fyO1NSqmjcIuyeF6PQ.jpeg","fullname":"Gabriel Pimenta de Freitas Cardoso","name":"GabrielPimenta99","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":22,"isUserFollowing":false,"primaryOrg":{"avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/67b22efa9b36260fe286e99f/5XWR_0JJv0gy6PRN9WDMF.png","fullname":"Dharma-AI","name":"Dharma-AI","type":"org","isHf":false,"details":"Specialized Small Language Models; Optimized Fine-Tuning; Efficient Inference","plan":"team"}}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9328820705413818},"editors":["GabrielPimenta99"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/67b22efa9b36260fe286e99f/vm2fyO1NSqmjcIuyeF6PQ.jpeg"],"reactions":[{"reaction":"👍","users":["ErickvL"],"count":1}],"isReport":false,"parentCommentId":"6a6b8fcace3e4a2fd6c3dc17"}}]},{"id":"6a6c42007f3b7929687d3a8f","author":{"_id":"66f15a00f7d3e809429a0d0e","avatarUrl":"/avatars/76248e5f0f20e0b0e8e95336e5abb52c.svg","fullname":"blacker","name":"blacker521","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false},"createdAt":"2026-07-31T06:34:40.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"Scheduling is the core bottleneck of this problem. A CPU-like isolation mechanism, or a sandbox-based architecture on GPUs, could significantly alleviate this issue.","html":"<p>Scheduling is the core bottleneck of this problem. A CPU-like isolation mechanism, or a sandbox-based architecture on GPUs, could significantly alleviate this issue.</p>\n","updatedAt":"2026-07-31T06:34:40.079Z","author":{"_id":"66f15a00f7d3e809429a0d0e","avatarUrl":"/avatars/76248e5f0f20e0b0e8e95336e5abb52c.svg","fullname":"blacker","name":"blacker521","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9256368279457092},"editors":["blacker521"],"editorAvatarUrls":["/avatars/76248e5f0f20e0b0e8e95336e5abb52c.svg"],"reactions":[],"isReport":false},"replies":[{"id":"6a709886abfb77b0f07169d3","author":{"_id":"6825e93ede3594c18b8d97ac","avatarUrl":"/avatars/506583318c7aa57f91ddfaa554e5a377.svg","fullname":"Breno de Almeida Beleza","name":"BrenoBeleza","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":9,"isUserFollowing":false},"createdAt":"2026-08-03T13:32:54.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"Good point, isolation is a real part of the picture. CPU-style boundaries (and the GPU sandboxing now landing, like the Kata Containers work) are what make it safe to co-locate unrelated workloads on the same card without fault or memory-contention risk, which widens the set of placements a scheduler is even allowed to make.\n\nTaking that a step further: once co-location is safe, we have to decide which workload runs on which GPU, at what time, at what priority, given that each one wants something different from the hardware (memory footprint, latency tolerance, duration). Isolation gives you a bigger, safer decision space; something still has to pick the right point in it. And rigid partitioning can even cut utilization, since an idle slice can't lend capacity to a busy neighbor.\n\nThat's exactly the direction we're taking next: an intelligent solution for job scheduling. We've got a follow-up article on it in the works, which will be published next week. Would be curious to get your read when it's out.","html":"<p>Good point, isolation is a real part of the picture. CPU-style boundaries (and the GPU sandboxing now landing, like the Kata Containers work) are what make it safe to co-locate unrelated workloads on the same card without fault or memory-contention risk, which widens the set of placements a scheduler is even allowed to make.</p>\n<p>Taking that a step further: once co-location is safe, we have to decide which workload runs on which GPU, at what time, at what priority, given that each one wants something different from the hardware (memory footprint, latency tolerance, duration). Isolation gives you a bigger, safer decision space; something still has to pick the right point in it. And rigid partitioning can even cut utilization, since an idle slice can't lend capacity to a busy neighbor.</p>\n<p>That's exactly the direction we're taking next: an intelligent solution for job scheduling. We've got a follow-up article on it in the works, which will be published next week. Would be curious to get your read when it's out.</p>\n","updatedAt":"2026-08-03T13:32:54.414Z","author":{"_id":"6825e93ede3594c18b8d97ac","avatarUrl":"/avatars/506583318c7aa57f91ddfaa554e5a377.svg","fullname":"Breno de Almeida Beleza","name":"BrenoBeleza","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":9,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9595561623573303},"editors":["BrenoBeleza"],"editorAvatarUrls":["/avatars/506583318c7aa57f91ddfaa554e5a377.svg"],"reactions":[{"reaction":"👍","users":["ErickvL","GabrielPimenta99"],"count":2}],"isReport":false,"parentCommentId":"6a6c42007f3b7929687d3a8f"}}]},{"id":"6a7703cfc961dfc90e3e3ecb","createdAt":"2026-08-08T10:24:15.000Z","type":"comment","data":{"edited":true,"hidden":true,"hiddenBy":"","latest":{"raw":"This comment has been hidden","html":"This comment has been hidden","updatedAt":"2026-08-19T14:05:19.080Z"},"numEdits":0,"editors":[],"editorAvatarUrls":[],"reactions":[]}},{"id":"6a7b322f18c8c3ca299106af","author":{"_id":"6a5f540e8a30e0660988ea8f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/U9AwsZyvT017uoE5buqC0.jpeg","fullname":"Mike Coville","name":"scifiscrivener","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false},"createdAt":"2026-08-11T14:31:11.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"I expect we will see GPU rental systems become a thing. There are a lot of high-end GPUs sitting in consumer computers doing absolutely nothing for 90% of the day. Imagine if individuals could allow a training model to access their GPU remotely and in return pay them something in return. Yes, remote GPUs is not optimal, but with the consumer taking on the maintenance, power bill, and ownership of the GPU, any use the training model gets is a net positive.","html":"<p>I expect we will see GPU rental systems become a thing. There are a lot of high-end GPUs sitting in consumer computers doing absolutely nothing for 90% of the day. Imagine if individuals could allow a training model to access their GPU remotely and in return pay them something in return. Yes, remote GPUs is not optimal, but with the consumer taking on the maintenance, power bill, and ownership of the GPU, any use the training model gets is a net positive.</p>\n","updatedAt":"2026-08-11T14:31:11.484Z","author":{"_id":"6a5f540e8a30e0660988ea8f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/U9AwsZyvT017uoE5buqC0.jpeg","fullname":"Mike Coville","name":"scifiscrivener","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.964832603931427},"editors":["scifiscrivener"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/U9AwsZyvT017uoE5buqC0.jpeg"],"reactions":[],"isReport":false}}],"status":"open","isReport":false,"pinned":false,"locked":false,"collection":"community_blogs"},"contextAuthors":["ErickvL","GabrielPimenta99","gustavolucchetti"],"primaryEmailConfirmed":false,"discussionRole":0,"acceptLanguages":["*"],"withThread":true,"cardDisplay":false,"repoDiscussionsLocked":false,"hideComments":true}">

The next frontier is managing an entire fleet of agents and there is nothing stopping it.

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• ](/GabrielPimenta99)
• 1 reply
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Article author [27 days ago](#6a6d0a73f2f02aadd53c048e)

Agreed. And the part that isn't priced in yet: a fleet of agents makes per-token cost scale linearly with the business. At some point large enterprises stop paying that curve and bring the models in-house — which doesn't remove the cost, it just trades a vendor price list for a utilization number. And agent fleets are brutal on utilization: real-time inference under latency SLAs, batch jobs, retraining, evals — all bursty, all fighting over the same finite pool. Managing the fleet is going to be, in large part, a GPU scheduling problem.

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👍
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Scheduling is the core bottleneck of this problem. A CPU-like isolation mechanism, or a sandbox-based architecture on GPUs, could significantly alleviate this issue.

• See translation
• [

• ](/BrenoBeleza)
• 1 reply
• ·

Good point, isolation is a real part of the picture. CPU-style boundaries (and the GPU sandboxing now landing, like the Kata Containers work) are what make it safe to co-locate unrelated workloads on the same card without fault or memory-contention risk, which widens the set of placements a scheduler is even allowed to make.

Taking that a step further: once co-location is safe, we have to decide which workload runs on which GPU, at what time, at what priority, given that each one wants something different from the hardware (memory footprint, latency tolerance, duration). Isolation gives you a bigger, safer decision space; something still has to pick the right point in it. And rigid partitioning can even cut utilization, since an idle slice can't lend capacity to a busy neighbor.

That's exactly the direction we're taking next: an intelligent solution for job scheduling. We've got a follow-up article on it in the works, which will be published next week. Would be curious to get your read when it's out.

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I expect we will see GPU rental systems become a thing. There are a lot of high-end GPUs sitting in consumer computers doing absolutely nothing for 90% of the day. Imagine if individuals could allow a training model to access their GPU remotely and in return pay them something in return. Yes, remote GPUs is not optimal, but with the consumer taking on the maintenance, power bill, and ownership of the GPU, any use the training model gets is a net positive.

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