Kimi K2.5
Moonshot AI's 1-trillion-parameter MoE with 32B active parameters and a multimodal agent-swarm mode. Around 595GB on disk, aimed at serious home labs and small clusters.
By Moonshot AI · China
Updated 2026-09-15
When to pick this model
- Multi-agent orchestration with swarm-mode coordination
- Frontier-scale local inference on a home lab cluster
- Long-context multimodal workflows up to 256K tokens
- Research into trillion-parameter models
- Replacing closed APIs at the high end
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 600 GB |
| Q5_K_M | 720 GB |
| Q8_0 | 1080 GB |
| FP16 (no quantization) | 2000 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 K2.5 is server-class even at Q4_K_M (600 GB). Stepping up to Q8_0 nearly doubles the footprint to 1080 GB, and unquantized FP16 weights take 2000 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Kimi K2.5 needs roughly 700 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 tokens/sec on entry-level GPUs, on the order of 4 tokens/sec on a mid-range card, and up to 12 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Kimi K2.5 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 K2.5 |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 600 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 600 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 600 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 600 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 600 GB at Q4_K_M |
Which hardware should you buy to run Kimi K2.5?
To run Kimi K2.5 locally at Q4, you need ~600 GB for Q4 weights alone. Hardware option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395). This model exceeds the practical GPU memory of this mini PC. Choose a smaller model or larger infrastructure.
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Strengths
- Genuine 1-trillion-parameter open-weight model
- Built-in agent swarm coordination mode
- 256K context with multimodal input
- Only 32B active parameters per token
Limitations
- ~600GB in Q4 demands a small cluster
- Modified MIT license needs legal review for commercial use
- Operational complexity is extreme
- Power and cooling budget rules out most home setups
Typical workloads
In our catalog grid, Kimi K2.5 is filed under Home-Lab Frontier, Complex Agents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
The 250k-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. The Modified MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE 1T/32B active · multimodal · 'agent swarm' mode · 256k ctx
Training: The largest practical open-weight model.
The largest practical open-weight model in 2026, for teams that can host it.
Quick start
Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.
Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.
# HuggingFace : moonshotai/Kimi-K2.5Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- 30-day refund
Similar models worth comparing
Frequently asked questions
How much VRAM does Kimi K2.5 need?
At the recommended Q4_K_M quantization, Kimi K2.5 needs about 600 GB of VRAM. Q8_0 takes 1080 GB, and unquantized FP16 weights take 2000 GB.
Can Kimi K2.5 run without a GPU?
Yes — with roughly 700 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 K2.5 support?
Kimi K2.5 supports a 250k-token context window (256,000 tokens).
Can I use Kimi K2.5 commercially?
Yes. Kimi K2.5 is released under Modified MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Kimi K2.5 on consumer hardware?
Our compatibility engine estimates on the order of 4 tokens/sec on a mid-range GPU and up to 12 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Kimi K2.5 should I download first?
Start with Q4_K_M (600 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.