Kimi K3
By Moonshot AI · China
Updated 2026-08-28
Overview
Kimi K3 is Moonshot AI's giant MoE model — 2.8T total parameters with 16 of 896 experts active per token — offering a native 1M-token context for multimodal, long-horizon coding and reasoning.
When to pick this model
- Frontier-level long-horizon coding agents needing massive context
- Multimodal reasoning tasks spanning vision and text
- Document or codebase analysis requiring the full 1M-token window
- Deployments on very large multi-GPU clusters chasing top-tier capability
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 1624 GB |
| Q5_K_M | 1988 GB |
| Q8_0 | 2996 GB |
| FP16 (no quantization) | 5600 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Kimi K3 is server-class even at Q4_K_M (1624 GB). Stepping up to Q8_0 nearly doubles the footprint to 2996 GB, and unquantized FP16 weights take 5600 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Kimi K3 needs roughly 3640 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1.5 tokens/sec on entry-level GPUs, on the order of 2.5 tokens/sec on a mid-range card, and up to 5 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Kimi K3 to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Kimi K3 |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 1624 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 1624 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 1624 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 1624 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 1624 GB at Q4_K_M |
Which GPU should you buy to run Kimi K3?
To run Kimi K3 locally at Q4, you need ~1624 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Strengths
- Native 1M-token context window
- Multimodal support (vision plus text)
- Strong long-horizon coding and reasoning capability
- Open weights available for self-hosted deployment
Limitations
- Colossal VRAM footprint (~1.6TB at Q4) — out of reach outside large clusters
- Low inference throughput
- Custom Kimi License — review terms for your use case
Typical workloads
In our catalog grid, Kimi K3 is filed under Frontier Scale, Long-Horizon Code, Multimodal Reasoning — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks; vision-language work — screenshots, charts, scanned documents.
The 976k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. It ships under the Kimi License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: MoE, 2.8T parameters · 16 of 896 experts active · 1M-token native context · multimodal
Training: Moonshot AI, successor to Kimi K2.6. A frontier open-weight model built for agents and long-horizon reasoning.
A frontier open-weight MoE model with genuine 1M-context and reasoning power, but only viable on serious cluster infrastructure.
Quick start
ollama run kimi-k3Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Kimi K3 need?
At the recommended Q4_K_M quantization, Kimi K3 needs about 1624 GB of VRAM. Q8_0 takes 2996 GB, and unquantized FP16 weights take 5600 GB.
Can Kimi K3 run without a GPU?
Yes — with roughly 3640 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Kimi K3 support?
Kimi K3 supports a 976k-token context window (1,000,000 tokens).
Can I use Kimi K3 commercially?
Kimi K3 ships under the Kimi License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Kimi K3 on consumer hardware?
Our compatibility engine estimates on the order of 2.5 tokens/sec on a mid-range GPU and up to 5 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Kimi K3 should I download first?
Start with Q4_K_M (1624 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.