gpu
| Date | Project Name | 🎉 | ? | Tags |
|---|---|---|---|---|
| 01/27 |
GoGPU v0.12.0
* [GoGPU v0.12.0](https://github.com/gogpu/gogpu) – Pure Go GPU computing ecosystem offering WebGPU-compatible APIs with selectable Rust or native backends and zero CGO.
Pure Go GPU computing ecosystem offering WebGPU-compatible APIs with selectable Rust or native backends and zero CGO.
|
7
|
Go 133 ⭐53 days old |
golang go graphics gpu game-development |
| 01/26 |
Tensor Fusion v1.54.10
* [Tensor Fusion v1.54.10](https://github.com/NexusGPU/tensor-fusion) – State-of-the-art GPU virtualization and pooling solution that optimizes GPU cluster utilization.
State-of-the-art GPU virtualization and pooling solution that optimizes GPU cluster utilization.
|
5
|
Go 117 ⭐439 days old |
golang ai go gpu amd-gpu gpu-pooling gpu-scheduling |
| 01/24 |
GoGPU v0.11.2
* [GoGPU v0.11.2](https://github.com/gogpu/gogpu) – Pure Go GPU computing ecosystem offering WebGPU-compatible APIs with selectable Rust or native backends and zero CGO.
Pure Go GPU computing ecosystem offering WebGPU-compatible APIs with selectable Rust or native backends and zero CGO.
|
5
|
Go 133 ⭐53 days old |
golang go graphics gpu game-development |
| 01/24 |
Kubernetes AI Toolchain Operator (KAITO) v0.8.1
* [Kubernetes AI Toolchain Operator (KAITO) v0.8.1](https://github.com/kaito-project/kaito) – Operator automating AI/ML model inference and tuning workloads in Kubernetes clusters with GPU auto-provisioning and large model management.
Operator automating AI/ML model inference and tuning workloads in Kubernetes clusters with GPU auto-provisioning and large model management.
|
6
|
Go 866 ⭐870 days old |
golang ai kubernetes go operator gpu |
| 01/20 |
Grove v0.1.0-alpha.5
* [Grove v0.1.0-alpha.5](https://github.com/ai-dynamo/grove) – Kubernetes API providing a single declarative interface to orchestrate multi-node AI inference with topology-aware placement, hierarchical gang scheduling, and autoscaling.
Kubernetes API providing a single declarative interface to orchestrate multi-node AI inference with topology-aware placement, hierarchical gang scheduling, and autoscaling.
|
2
|
Go 157 ⭐488 days old |
golang go gpu agentic auto-scaling auto-scaling-group gang-scheduling |
| 01/20 |
GPUd v0.10.0-test.8
* [GPUd v0.10.0-test.8](https://github.com/leptonai/gpud) – GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.
GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.
|
4
|
Go 471 ⭐522 days old |
monitoring kubernetes go gpu nvidia nvidia-gpu |
| 01/19 |
GPUd v0.10.0-test.7
* [GPUd v0.10.0-test.7](https://github.com/leptonai/gpud) – GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.
GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.
|
4
|
Go 471 ⭐522 days old |
monitoring kubernetes go gpu nvidia nvidia-gpu |
| 01/19 |
GPUd v0.10.0-test.5
* [GPUd v0.10.0-test.5](https://github.com/leptonai/gpud) – GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.
GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.
|
4
|
Go 471 ⭐522 days old |
monitoring kubernetes go gpu nvidia nvidia-gpu |
| 01/17 |
Tensor Fusion v1.55.0
* [Tensor Fusion v1.55.0](https://github.com/NexusGPU/tensor-fusion) – State-of-the-art GPU virtualization and pooling solution that optimizes GPU cluster utilization.
State-of-the-art GPU virtualization and pooling solution that optimizes GPU cluster utilization.
|
7
|
Go 117 ⭐439 days old |
golang ai go gpu amd-gpu gpu-pooling gpu-scheduling |
| 01/16 |
RapidFire AI v0.12.9
* [RapidFire AI v0.12.9](https://github.com/RapidFireAI/rapidfireai) – Hyperparallelized, shard-based experiment framework for rapid LLM customization, RAG, context engineering, and fine-tuning with real-time control.
Hyperparallelized, shard-based experiment framework for rapid LLM customization, RAG, context engineering, and fine-tuning with real-time control.
|
5
|
JavaScript 134 ⭐205 days old |
javascript ai llm experimentation gpu mlflow |
| 01/16 |
Grove v0.1.0-alpha.4
* [Grove v0.1.0-alpha.4](https://github.com/ai-dynamo/grove) – Kubernetes API providing a single declarative interface to orchestrate multi-node AI inference with topology-aware placement, hierarchical gang scheduling, and autoscaling.
Kubernetes API providing a single declarative interface to orchestrate multi-node AI inference with topology-aware placement, hierarchical gang scheduling, and autoscaling.
|
2
|
Go 157 ⭐488 days old |
golang go gpu agentic auto-scaling auto-scaling-group gang-scheduling |
| 01/15 |
GPUd v0.10.0-test.3
* [GPUd v0.10.0-test.3](https://github.com/leptonai/gpud) – GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.
GPU-focused monitoring and diagnostics tool that detects GPU and fabric errors and reports critical system metrics.
|
4
|
Go 471 ⭐522 days old |
monitoring kubernetes go gpu nvidia nvidia-gpu |
| 01/15 |
GPU Hot v1.6.1
* [GPU Hot v1.6.1](https://github.com/psalias2006/gpu-hot) – Real-time NVIDIA GPU monitoring dashboard with sub-second updates and multi-GPU support accessible from any browser.
Real-time NVIDIA GPU monitoring dashboard with sub-second updates and multi-GPU support accessible from any browser.
|
6
|
JavaScript 1317 ⭐103 days old |
javascript docker flask charts gpu cuda |