Moonshot AI released Kimi K3 on July 16, 2026 with 2.8 trillion parameters, making it the largest open-weight model published to date, with weights scheduled for July 27 under a modified MIT licence. Open weights are not the same as runnable weights. This page works through what serving it actually requires.
The Model
| Property | Value |
|---|---|
| Total parameters | 2.8 trillion |
| Architecture | Sparse mixture of experts, approximately 16 of 896 experts active per token |
| Context window | 1,000,000 tokens |
| Modality | Text, image, and video understanding |
| Licence | Modified MIT |
| Notable architecture | Kimi Delta Attention (KDA), Attention Residuals (AttnRes) |
| Hosted API pricing | $3 / $15 per million input / output tokens |
Memory Requirements by Precision
The rule that governs everything here: all 2.8 trillion parameters must be resident in memory, even though only about 16 of 896 experts activate for any given token. Sparse routing buys compute efficiency, not memory efficiency. Approximate weight-only footprints, before key-value cache and runtime overhead:
| Precision | Bytes per parameter | Approximate weight memory |
|---|---|---|
| BF16 | 2 | ~5,600 GB |
| FP8 | 1 | ~2,800 GB |
| MXFP4 / 4-bit | 0.5 | ~1,400 GB |
At MXFP4, the format the community has focused on for K3, you need roughly 1.4 terabytes of memory for weights alone. Add key-value cache for a context window of this size and realistic serving overhead, and the practical figure is higher still.
What That Means in Hardware
| Configuration | Memory | Runs K3 at MXFP4? |
|---|---|---|
| RTX 5090 | 32GB | No, off by roughly 44x |
| Mac Studio M5 Max 128GB | 128GB | No, off by roughly 11x |
| DGX Spark | 128GB | No, off by roughly 11x |
| 8x H100 80GB node | 640GB | No, off by roughly 2x |
| 8x H200 141GB node | ~1,128GB | Marginal, tight with cache |
| 16x H100 or 2x H200 node | 1,280GB+ | Yes, with headroom |
Serving Kimi K3 is a multi-node data-centre workload. No workstation configuration at any price approaches it. The practical options are the hosted Moonshot API, an inference provider that has stood it up, or a rented multi-GPU cluster.
Why Release It At All
Because the weights being public changes what is possible even for people who cannot run them. Researchers can study the architecture, KDA and AttnRes in particular. Distillation into smaller models becomes possible. Enterprises with real infrastructure can deploy it inside their own perimeter with no vendor dependency. And smaller derivative models trained on K3 outputs will run on hardware ordinary teams own.
The Broader Pattern
Open-weight frontier models are growing faster than the hardware available to run them. GLM-5.2 at 753B and MiniMax M3 at 428B are already beyond workstation range. The gap between what is published and what an individual can serve is widening, and the memory shortage documented in the device price report is widening it further. The counter-trend is models such as Qwen3-Coder-Next at 80B total and 3B active, which deliberately target hardware people own.
Brand Visibility Implications
Kimi K3 will mostly be consumed through hosted endpoints and inside enterprises with serious infrastructure. The second category matters most for measurement: a bank or pharmaceutical company serving K3 privately generates brand-relevant answers with no external observability at all. As frontier open weights get better, private frontier-grade inference becomes normal enterprise practice. See air-gapped deployment statistics and the Kimi K3 release brief.
Methodology
Vendor specifications come from NVIDIA, Apple, and model-card publications. Throughput figures aggregate community benchmark reporting from the llama.cpp discussions, the MLX repository, and published independent test suites. Single-stream decode unless stated otherwise. Ranges rather than point values are used wherever independent runs disagree, which is most of the time: quantisation format, prompt length, thermal state, and runtime version each move these numbers by more than the differences being measured. Treat every figure as an order-of-magnitude guide, not a specification. Updated quarterly.
How Presenc AI Helps
Presenc AI tracks brand representation across frontier open-weight models including hosted Kimi endpoints, so teams can see what these models say about them without provisioning the infrastructure to run them.