Internal benchmark
Distributed rendering, 38x faster
We rendered a 120-frame Full HD Blender scene. On a laptop CPU, one frame took 2 minutes 28 seconds, which puts all 120 frames at about 296 minutes. Split across four GPU nodes on Symbola's GPU/CPU Grid, the full job finished in 7.76 minutes, end to end. We also moved a 10 GB file through Bandwidth Pool at three times the speed of a direct download.
GPU/CPU Grid: distributed Blender rendering
The job: 120 frames at 1920 × 1080 in Blender. In the baseline, a laptop rendered on its CPU alone; we timed the first frame and multiplied by 120. In the Symbola run, GPU/CPU Grid split the frame range into four chunks of 30, rendered them in parallel on four GPU nodes and reassembled the output on the laptop.
| Run | Setup | Wall-clock time |
|---|---|---|
| Baseline | Laptop, 10th-gen Intel Core i5, CPU only. Frame 1 measured at 148 s, total estimated for 120 frames | 296 min (4.93 h), estimated |
| Symbola | 4 laptops across India, each with an NVIDIA RTX 4050 Laptop GPU (6 GB VRAM) and 8 GB RAM, 30 frames per node | 7.76 min, measured end to end |
| Setting | Value |
|---|---|
| Software | Blender 4.4.3 |
| Scene | “Candy Bounce”, a Blender Geometry Nodes demo |
| Output | 120 frames, 1920 × 1080, 24 fps, PNG |
| Samples | 60 per frame |
| Run date | 6 January 2026 |
The 7.76 minutes covers everything from submitting the job on the laptop to having all 120 frames back on it. GPU rendering itself took 61 to 101 seconds per node. The rest went to packaging the frames, merging them in order and transferring them back to the laptop.
Watch the recorded rendering run.
Bandwidth Pool: 10 GB transfer
The job: download a 10 GB file. In the baseline, it came over a single direct connection. In the Symbola run, Bandwidth Pool streamed it over several encrypted paths through contributor nodes at the same time.
| Run | Setup | Average speed |
|---|---|---|
| Baseline | Standard direct download | 430.50 Mbps |
| Symbola | Multi-node streamed download | 1,301.28 Mbps |
Watch the recorded transfer run.
How we measured
Both runs were recorded on video and logged by our own benchmark framework, which captures telemetry from every node involved. The telemetry consoles on the homepage demo show the logged output alongside each recording.
Want the raw logs or the full hardware list? Ask us and we will send them.
Limits, stated plainly
- We ran these tests ourselves. They have not been reproduced by a third party yet, and we would welcome anyone who wants to.
- The rendering baseline is a laptop rendering on CPU, not a single modern GPU, and its total is estimated from one measured frame. A GPU baseline would shrink the multiple. We will publish that comparison when it is done.
- Rendering separate frames is a naturally parallel job. Workloads that need constant communication between nodes will see smaller gains.
- Both runs were finished jobs, a render and a file transfer, not live streams. Continuous real-time workloads are the next milestone on the roadmap, and we will benchmark them the same way.
The products behind the numbers
Read how GPU/CPU Grid splits and schedules compute jobs, and how Bandwidth Pool spreads a transfer over several paths.