K3Nova
Name the mode first
Decide whether you are using hosted Kimi, running the K3Nova web workspace, testing a client against a hosted endpoint, or self-hosting model weights.
Decision answer
For hosted Kimi K3, Kimi Code, Kimi Work, or K3Nova, you do not need a local GPU to use the model. You need a current browser or CLI, stable network, and authorized model access.
For full Kimi K3 self-hosting, treat the project as cluster-scale. The official model data lists 2.8T total parameters, MXFP4 weights, 896 experts, 16 selected experts per token, and a 1M-token context window. A single workstation GPU is not the full-model answer.
Decision checklist
Use this section to keep the next step practical before you touch accounts, files, infrastructure, or hardware.
| Check | Look for | Why it matters |
|---|---|---|
| Read the official source | Start with the model card, help center, docs, or official product page before trusting summaries. | Model names, limits, and prices change. |
| Name the path | Decide whether you need a hosted app, API call, local client, or full self-managed deployment. | Different paths have different costs and account boundaries. |
| Check the boundary | Keep login, payment, API keys, private files, and account settings on the official surface. | A public guide should help you choose, not handle sensitive actions. |
| Keep evidence | Save the source link, date, version, and practical next step that shaped your decision. | This makes later review easier for a team. |
Hardware checklist by mode
| Mode | Local hardware | Main limit |
|---|---|---|
| Kimi web, Kimi Code, Kimi Work, or K3Nova | Current device and stable network | Account, quota, endpoint, and request size |
| Local K3Nova development | Node runtime, browser, and enough RAM for assets | Server config and local port conflicts |
| One RTX 6000 Ada | 48 GB VRAM workstation GPU | Useful for smaller local models, not full Kimi K3 |
| Full self-hosting | Distributed accelerator cluster | Weight memory, context cache, interconnect, and throughput |
Evaluation path
Do memory math before shopping
Even low-precision weight estimates land around terabytes before cache, activations, routing overhead, and concurrency.
Check loader support
Do not buy hardware until weights, license, checksums, loader support, and a reproducible serving path are confirmed.
Prototype with the API
Use a hosted endpoint to test quality and prompt shape before committing to hardware.
Limits to keep visible
Hosted use
A normal laptop can use the web product or K3Nova workspace because model inference happens elsewhere.
RTX 6000 Ada
A 48 GB RTX 6000 Ada is a strong workstation card for many local AI tasks, but one card cannot hold the full Kimi K3 weights.
Self-hosting
Plan for multiple accelerators, high-bandwidth interconnect, host RAM, fast storage, cooling, monitoring, and workload-specific evaluation.
FAQ
Do I need a GPU for K3Nova?
No. The web workspace does not load Kimi K3 weights onto your device.
Can an RTX 6000 Ada run full Kimi K3?
Not by itself. Its 48 GB VRAM is far below the full-model weight floor.
What should I test before buying hardware?
Test model quality, prompt length, concurrency, and serving support with a hosted or rented setup first.