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markups, making costs unpredictable while offering limited live visibility into progress. By renting modern cloud GPU instances (such as ",[176,177,178],"strong",{},"RunPod",", ",[176,181,182],{},"Vast.ai",", or ",[176,185,186],{},"AutoDL","), you can ",[176,189,190],{},"spin up single or multi-card RTX 4090 \u002F 3090 instances, pay by the second or hour",", and destroy them immediately when rendering finishes—often cutting total compute expenditure by 70% to 80% compared to legacy farms.",[169,193,194,195,198,199,202],{},"Combined with danio's ",[176,196,197],{},"single-command joining"," and ",[176,200,201],{},"resilient lease scheduling",", cloud nodes automatically take on workloads just like local workstations in the next room.",[164,204,206],{"id":205},"hardware-gpu-selection-guide","Hardware & GPU Selection Guide",[169,208,209,210,219],{},"Blender Cycles heavily relies on CUDA \u002F OptiX ray-tracing cores and high core frequencies. When picking hardware, we recommend consulting the official ",[176,211,212],{},[213,214,218],"a",{"href":215,"rel":216},"https:\u002F\u002Fopendata.blender.org\u002F",[217],"nofollow","Blender Open Data"," benchmark leaderboard:",[221,222,223,234,240,246],"ul",{},[224,225,226,229,230,233],"li",{},[176,227,228],{},"Speed flagship recommendation (RTX 5090 \u002F RTX 4090)",": Dominating the top tier of ",[213,231,218],{"href":215,"rel":232},[217],". The RTX 5090 (32 GB VRAM) and RTX 4090 (24 GB VRAM) deliver unmatched OptiX ray-tracing throughput at a fraction of the cost of enterprise compute accelerators (typically $0.35–$0.89\u002Fhr), offering unbeatable price-to-performance.",[224,235,236,239],{},[176,237,238],{},"Massive scenes & heavy VRAM recommendation (RTX 6000 Ada \u002F RTX Pro 6000)",": For large-scale architectural projects, heavy 8K UDIM texture sets, or dense volumetric simulation caches, choose professional 48 GB workstation cards (such as the RTX 6000 Ada \u002F Pro 6000 series) to completely avoid CUDA Out of Memory crashes.",[224,241,242,245],{},[176,243,244],{},"High-value alternative (RTX 3090)",": Features a generous 24 GB VRAM with ultra-low rental rates (~$0.20–$0.30\u002Fhr), ideal for overnight batch rendering and long shot sequences.",[224,247,248,251],{},[176,249,250],{},"Avoid legacy compute cards",": Older cards (such as P40 or T4) and pure CPU instances lack modern RT cores and render many times slower.",[164,253,255],{"id":254},"recommended-platforms-comparison","Recommended Platforms & Comparison",[257,258,261],"div",{"className":259},[260],"whitespace-nowrap",[262,263,264,284],"table",{},[265,266,267],"thead",{},[268,269,270,275,278,281],"tr",{},[271,272,274],"th",{"align":273},"left","Platform",[271,276,277],{"align":273},"Region",[271,279,280],{"align":273},"Highlights",[271,282,283],{"align":273},"Recommended GPUs & Estimated Rates",[285,286,287,328,357],"tbody",{},[268,288,289,303,306,319],{},[290,291,292,298,299],"td",{"align":273},[176,293,294],{},[213,295,178],{"href":296,"rel":297},"https:\u002F\u002Frunpod.io?ref=7nxf8hxb",[217]," ",[300,301,302],"em",{},"(referral link)",[290,304,305],{"align":273},"Global",[290,307,308,309,312,313,316,318],{"align":273},"• Sleek UI with Web Terminal and 1-click SSH",[310,311],"br",{},"• True per-second billing with ",[176,314,315],{},"zero egress fees (0 Egress)",[310,317],{},"• Reliable Secure Cloud or budget Community Cloud",[290,320,321,322,324,325,327],{"align":273},"RTX 4090",[310,323],{},"Community: ~$0.74\u002Fhr",[310,326],{},"Secure: ~$1.10\u002Fhr",[268,329,330,340,342,351],{},[290,331,332,298,338],{"align":273},[176,333,334],{},[213,335,182],{"href":336,"rel":337},"https:\u002F\u002Fcloud.vast.ai\u002F?ref_id=860757",[217],[300,339,302],{},[290,341,305],{"align":273},[290,343,344,345,347,348,350],{"align":273},"• Largest decentralized GPU marketplace with lowest rates",[310,346],{},"• Supports interruptible bid instances (saving 30%–50% more)",[310,349],{},"• Danio's 2-minute lease failover protects against instance preemption",[290,352,353,354,356],{"align":273},"RTX 4090: ~$0.35 – $0.55\u002Fhr",[310,355],{},"RTX 3090: ~$0.20 – $0.30\u002Fhr",[268,358,359,367,370,376],{},[290,360,361],{"align":273},[176,362,363],{},[213,364,186],{"href":365,"rel":366},"https:\u002F\u002Fwww.autodl.com",[217],[290,368,369],{"align":273},"China",[290,371,372,373,375],{"align":273},"• Direct domestic network access; Alipay\u002FWeChat pay",[310,374],{},"• Instant spin-up with minimal latency",[290,377,378],{"align":273},"RTX 4090: ~¥1.8 – ¥2.5\u002Fhr",[164,380,382],{"id":381},"prerequisites","Prerequisites",[384,385,386,403,418],"ol",{},[224,387,388,391,392,398,399,402],{},[176,389,390],{},"S3 Object Storage Workspace",": Cloud nodes cannot reach local SMB network shares. The workspace must use S3 storage. ",[176,393,394,395],{},"We strongly recommend ",[213,396,397],{"href":42},"Cloudflare R2"," for its ",[176,400,401],{},"$0 egress fees",", avoiding unexpected transfer bills when workers pull assets concurrently.",[224,404,405,408,409,413,414,417],{},[176,406,407],{},"Public HTTPS URL for Host",": Cloud nodes must reach your host via a public URL (such as ",[410,411,412],"code",{},"https:\u002F\u002Fdanio.yourdomain.com","), see ",[213,415,416],{"href":51},"Remote Devices",".",[224,419,420,423,424,428,429,432,433,436],{},[176,421,422],{},"Linux Blender Resource Pack",": Most rental platforms run Linux (Ubuntu). In the host under ",[425,426,427],"ui",{},"Workspace details"," → ",[425,430,431],{},"Resource packs",", import a Linux version of Blender (see ",[213,434,431],{"href":435},"\u002Fen\u002Fdocs\u002Fconcepts\u002Fruntimes#resource-packs","); joined nodes will download it automatically.",[164,438,440],{"id":439},"step-1-generate-joining-command-on-host","Step 1: Generate Joining Command on Host",[169,442,443,444,428,447,450],{},"On the danio host, go to ",[425,445,446],{},"Devices",[425,448,449],{},"Add device",":",[384,452,453,463,476,490,497,506,541],{},[224,454,455,456,459,460,417],{},"Enter a unified ",[425,457,458],{},"Name"," prefix, e.g., ",[410,461,462],{},"cloud-gpu",[224,464,465,466,469,470,475],{},"Set ",[425,467,468],{},"Permission"," to ",[176,471,472],{},[425,473,474],{},"None",": Cloud render nodes only need to pull chunks and upload rendered fragments. They do not need access to view other projects or configure the workspace.",[224,477,478,479,482,483,179,486,489],{},"Turn on ",[425,480,481],{},"Multiple devices",": A single token can be shared across multiple spun-up nodes, automatically numbering them ",[410,484,485],{},"cloud-gpu-01",[410,487,488],{},"cloud-gpu-02","...",[224,491,492,493,496],{},"Keep ",[425,494,495],{},"Render"," checked.",[224,498,465,499,469,502,505],{},[425,500,501],{},"Download danio",[425,503,504],{},"From official source"," (nodes download via GitHub CDN, which is fast and spares your host's upstream bandwidth).",[224,507,508,511,512,515,516],{},[176,509,510],{},"Configure Local Scratch Cache"," (Critical): Open ",[425,513,514],{},"Extra settings"," and specify the node's local NVMe scratch path:\n",[221,517,518,524,530],{},[224,519,520,521],{},"RunPod: ",[410,522,523],{},"\u002Fworkspace\u002Fcache",[224,525,526,527],{},"Vast.ai: ",[410,528,529],{},"\u002Froot\u002Fcache",[224,531,532,533,536,537,540],{},"AutoDL: ",[410,534,535],{},"\u002Froot\u002Fautodl-tmp\u002Fcache"," (do not place on ",[410,538,539],{},"\u002Froot\u002Fautodl-fs"," network drives)",[224,542,543,544,547,548,551],{},"Click ",[425,545,546],{},"Generate token",", select ",[425,549,550],{},"Linux"," as the system, and copy the full command.",[164,553,555],{"id":554},"step-2-launch-and-join-on-rental-platforms","Step 2: Launch and Join on Rental Platforms",[557,558,560],"h3",{"id":559},"deploying-on-runpod","Deploying on RunPod",[384,562,563,578,588,595,602,611,614],{},[224,564,565,566,570,571,574,575,417],{},"In the ",[213,567,569],{"href":296,"rel":568},[217],"RunPod dashboard"," (referral link), navigate to ",[176,572,573],{},"Pods"," and click ",[176,576,577],{},"Deploy",[224,579,580,581,584,585,417],{},"Select your GPU (e.g., 1x RTX 4090), and pick ",[176,582,583],{},"Community Cloud"," (budget) or ",[176,586,587],{},"Secure Cloud",[224,589,590,591,594],{},"Choose a template image (e.g. ",[410,592,593],{},"runpod\u002Fpytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04"," or standard Ubuntu).",[224,596,597,598,601],{},"Expand ",[176,599,600],{},"Container Disk"," (recommend at least 50 GB to store unpacked textures and job directories).",[224,603,604,605,428,608,417],{},"Launch the Pod. Once Running, click ",[176,606,607],{},"Connect",[176,609,610],{},"Start Web Terminal",[224,612,613],{},"Paste the danio join command copied from your host and press Enter.",[224,615,616,617,620],{},"The installer automatically installs dependencies, registers the node, and starts ",[410,618,619],{},"danio serve"," in the background.",[557,622,624],{"id":623},"deploying-on-vastai","Deploying on Vast.ai",[384,626,627,639,648,654,660,669,676],{},[224,628,629,630,634,635,638],{},"Log into the ",[213,631,633],{"href":336,"rel":632},[217],"Vast.ai dashboard"," (referral link) and open the ",[176,636,637],{},"Search"," filter.",[224,640,641,642,644,645,417],{},"Filter for ",[410,643,321],{},", and optionally check ",[176,646,647],{},"Datacenter",[224,649,465,650,653],{},[176,651,652],{},"Disk Space"," to at least 50 GB.",[224,655,656,657,417],{},"Select the official template ",[410,658,659],{},"nvidia\u002Fcuda:12.4.1-runtime-ubuntu22.04",[224,661,543,662,665,666,417],{},[176,663,664],{},"Rent",". Wait for the instance to become active under ",[176,667,668],{},"Instances",[224,670,671,672,675],{},"Click the ",[176,673,674],{},"SSH"," button on the instance, copy the SSH command, and connect from your local terminal.",[224,677,678],{},"Run the danio join command.",[557,680,682],{"id":681},"deploying-on-autodl","Deploying on AutoDL",[384,684,685,688,691,694],{},[224,686,687],{},"Log into the AutoDL console and rent an RTX 4090 instance.",[224,689,690],{},"Select Ubuntu 22.04 with CUDA 12.x.",[224,692,693],{},"Open JupyterLab terminal or connect via SSH.",[224,695,696,697,700],{},"Run the join command. Danio will automatically utilize the ",[410,698,699],{},"\u002Froot\u002Fautodl-tmp"," local data volume.",[164,702,704],{"id":703},"step-3-verification-rendering","Step 3: Verification & Rendering",[169,706,707,708,710],{},"Once connected, return to the host's ",[425,709,446],{}," page:",[221,712,713,722,733],{},[224,714,715,716,718,719,417],{},"The new machine will appear as ",[410,717,485],{}," with status ",[425,720,721],{},"Online",[224,723,724,725,728,729,732],{},"Open device details: ",[425,726,727],{},"Capabilities"," will display Cycles configured as ",[176,730,731],{},"OptiX"," with your GPU model detected.",[224,734,735,736,739,740,417],{},"If tasks are waiting in the queue, the node will immediately switch to ",[425,737,738],{},"Preparing"," (downloading Blender and missing assets) and then begin ",[425,741,742],{},"Rendering",[744,745,748],"callout",{"color":746,"icon":747},"info","i-lucide-shield-check",[169,749,750],{},"Cloud nodes pull only the files required for their currently assigned shot (via HTTP Range), encode finished shots into fragments uploaded directly to S3, and stream live previews back to the host.",[164,752,754],{"id":753},"fault-tolerance-preemption-protection","Fault Tolerance & Preemption Protection",[169,756,757],{},"When using on-demand or spot\u002Finterruptible instances, nodes can occasionally disconnect or get reclaimed by providers:",[221,759,760,766],{},[224,761,762,765],{},[176,763,764],{},"2-Minute Lease Protection",": danio grants a 2-minute dynamic lease for every chunk. If a server abruptly drops offline, the lease expires and the host reassigns uncompleted frames to other active workers.",[224,767,768,771,772,775],{},[176,769,770],{},"Completed Frame Protection",": In the task details, click any active chunk and select ",[425,773,774],{},"Recall this chunk…",". Checking \"Keep rendered frames\" encodes all completed frames up to that moment into a fragment immediately, handing only remaining frames to another node.",[164,777,779],{"id":778},"teardown-clean-up","Teardown & Clean Up",[744,781,784],{"color":782,"icon":783},"warning","i-lucide-triangle-alert",[169,785,786,787,469,790,793,794,797],{},"Raw intermediate frames remain on the temporary instance disk and disappear when the instance is terminated. Final encoded videos and fragment packages reside safely in your S3 store. If your pipeline requires raw image sequences, configure ",[425,788,789],{},"Output format",[425,791,792],{},"Tar sequence package (.tar)"," in ",[425,795,796],{},"Projects"," before submission.",[169,799,800],{},"When all shots finish rendering and the film is joined:",[384,802,803,806,812],{},[224,804,805],{},"Terminate\u002FDestroy the instance in your provider's dashboard (RunPod \u002F Vast.ai) to halt billing.",[224,807,808,809,811],{},"In the danio host ",[425,810,446],{}," page, open the offline device's details card.",[224,813,543,814,817,818,821],{},[425,815,816],{},"Block"," at the bottom, then hold ",[425,819,820],{},"Remove device"," for 2 seconds to purge it from the roster.",{"title":823,"searchDepth":824,"depth":825,"links":826},"",2,3,[827,828,829,830,831,832,837,838,839],{"id":166,"depth":824,"text":167},{"id":205,"depth":824,"text":206},{"id":254,"depth":824,"text":255},{"id":381,"depth":824,"text":382},{"id":439,"depth":824,"text":440},{"id":554,"depth":824,"text":555,"children":833},[834,835,836],{"id":559,"depth":825,"text":560},{"id":623,"depth":825,"text":624},{"id":681,"depth":825,"text":682},{"id":703,"depth":824,"text":704},{"id":753,"depth":824,"text":754},{"id":778,"depth":824,"text":779},"Rent on-demand RTX 4090 GPU nodes on RunPod, Vast.ai, or AutoDL, and connect with a single command.","md",{},{"icon":39},{"title":36,"description":840},"C9Z8Da_z3wPIrrnX1R_zjmJJyV6ywSvW2s8Z_det0U0",[847,849],{"title":31,"path":32,"stem":33,"description":848,"icon":34,"children":-1},"Colleagues' computers find the workspace on the local network, ask to join, and submit or render as needed.",{"title":41,"path":42,"stem":43,"description":850,"children":-1},"[object Object]",1794627696229]