Kimi K2.5

Deploy Kimi K2.5 (1T MoE multimodal) by Moonshot AI on Clore.ai GPUs

Kimi K2.5, released January 27, 2026 by Moonshot AI, is a 1-trillion parameter Mixture-of-Experts multimodal model with 32B active parameters per token. Built through continual pretraining on ~15 trillion mixed visual and text tokens atop the Kimi-K2-Base, it natively understands text, images, and video. K2.5 introduces Agent Swarm technology — coordinating up to 100 specialized AI agents simultaneously — and achieves frontier-level performance on coding (76.8% SWE-bench Verified), vision, and agentic tasks. Available under an open-weight license on HuggingFace.

Key Features

• 1T total / 32B active — 384-expert MoE architecture with MLA attention and SwiGLU

• Native multimodal — pre-trained on vision–language tokens; understands images, video, and text

• Agent Swarm — decomposes complex tasks into parallel sub-tasks via dynamically spawned agents

• 256K context window — process entire codebases, long documents, and video transcripts

• Hybrid reasoning — supports both instant mode (fast) and thinking mode (deep reasoning)

• Strong coding — 76.8% SWE-bench Verified, 73.0% SWE-bench Multilingual

Requirements

Kimi K2.5 is a massive model — the FP8 checkpoint is ~630GB. Self-hosting requires serious hardware.

Component	Quantized (GGUF Q2)	FP8 Full
GPU	1× RTX 4090 + 256GB RAM	8× H200 141GB
VRAM	24GB + CPU offload	1,128GB
RAM	256GB+	256GB
Disk	400GB SSD	700GB NVMe
CUDA	12.0+	12.0+

Clore.ai recommendation: For full-precision serving, rent 8× H200 (~$24–48/day). For quantized local inference, a single H100 80GB or even RTX 4090 + heavy CPU offloading works at reduced speed.

Quick Start with llama.cpp (Quantized)

The most accessible way to run K2.5 locally — using Unsloth's GGUF quantizations:

# Clone and build llama.cpp
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
cmake -B build -DGGML_CUDA=ON && cmake --build build --config Release -j

# Download quantized model (Q2_K_XL — 375GB, good quality/size balance)
huggingface-cli download unsloth/Kimi-K2.5-GGUF \
  Kimi-K2.5-UD-Q2_K_XL-00001-of-00005.gguf \
  Kimi-K2.5-UD-Q2_K_XL-00002-of-00005.gguf \
  Kimi-K2.5-UD-Q2_K_XL-00003-of-00005.gguf \
  Kimi-K2.5-UD-Q2_K_XL-00004-of-00005.gguf \
  Kimi-K2.5-UD-Q2_K_XL-00005-of-00005.gguf \
  --local-dir ./models

# Run inference (adjust --n-gpu-layers for your VRAM)
./build/bin/llama-server \
  -m ./models/Kimi-K2.5-UD-Q2_K_XL-00001-of-00005.gguf \
  --n-gpu-layers 10 \
  --threads 32 \
  --ctx-size 16384 \
  --host 0.0.0.0 --port 8080

Note: Vision is not yet supported in GGUF/llama.cpp for K2.5. For multimodal features, use vLLM.

vLLM Setup (Production — Full Model)

For production serving with full multimodal support:

# Install vLLM nightly (K2.5 requires latest)
pip install -U vllm --pre \
  --extra-index-url https://wheels.vllm.ai/nightly/cu129 \
  --extra-index-url https://download.pytorch.org/whl/cu129 \
  --index-strategy unsafe-best-match

Serve on 8× H200 GPUs

vllm serve moonshotai/Kimi-K2.5 \
  -tp 8 \
  --mm-encoder-tp-mode data \
  --tool-call-parser kimi_k2 \
  --reasoning-parser kimi_k2 \
  --trust-remote-code \
  --gpu-memory-utilization 0.90

Query with Text

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.5",
    messages=[
        {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
        {"role": "user", "content": "Write a FastAPI service with WebSocket support for real-time chat"}
    ],
    temperature=0.6,
    max_tokens=4096
)
print(response.choices[0].message.content)

Query with Image (Multimodal)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY", timeout=3600)

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.5",
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {"url": "https://example.com/diagram.png"}
            },
            {
                "type": "text",
                "text": "Describe this diagram in detail and extract all text."
            }
        ]
    }],
    max_tokens=2048
)
print(response.choices[0].message.content)

API Access (No GPU Required)

If self-hosting is overkill, use Moonshot's official API:

from openai import OpenAI

# Moonshot Platform — OpenAI-compatible API
client = OpenAI(
    api_key="your-moonshot-api-key",
    base_url="https://api.moonshot.ai/v1"
)

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "user", "content": "Explain the Agent Swarm architecture in Kimi K2.5"}
    ],
    temperature=0.6,
    max_tokens=2048
)
print(response.choices[0].message.content)

Tool Calling

K2.5 excels at agentic tool use:

import json
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

tools = [{
    "type": "function",
    "function": {
        "name": "search_code",
        "description": "Search a codebase for relevant files and functions",
        "parameters": {
            "type": "object",
            "required": ["query"],
            "properties": {
                "query": {"type": "string", "description": "Search query"}
            }
        }
    }
}]

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.5",
    messages=[{"role": "user", "content": "Find all authentication-related code in the project"}],
    tools=tools,
    tool_choice="auto",
    temperature=0.6
)

for tool_call in response.choices[0].message.tool_calls:
    print(f"Function: {tool_call.function.name}")
    print(f"Args: {json.loads(tool_call.function.arguments)}")

Docker Quick Start

# Using vLLM Docker with 8 GPUs
docker run --gpus all -p 8000:8000 \
  --ipc=host \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest \
  --model moonshotai/Kimi-K2.5 \
  --tensor-parallel-size 8 \
  --mm-encoder-tp-mode data \
  --tool-call-parser kimi_k2 \
  --reasoning-parser kimi_k2 \
  --trust-remote-code

Tips for Clore.ai Users

• API vs self-hosting tradeoff: Full K2.5 needs 8× H200 at ~$24–48/day. Moonshot's API is free-tier or pay-per-token — use API for exploration, self-host for sustained production loads.

• Quantized on single GPU: The Unsloth GGUF Q2_K_XL (~375GB) can run on an RTX 4090 ($0.5–2/day) with 256GB RAM via CPU offloading — expect ~5–10 tok/s. Good enough for personal use and development.

• Text-only K2 for budget setups: If you don't need vision, moonshotai/Kimi-K2-Instruct is the text-only predecessor — same 1T MoE but lighter to deploy (no vision encoder overhead).

• Set temperature correctly: Use temperature=0.6 for instant mode, temperature=1.0 for thinking mode. Wrong temperature causes repetition or incoherence.

• Expert Parallelism for throughput: On multi-node setups, use --enable-expert-parallel in vLLM for higher throughput. Check vLLM docs for EP configuration.

Troubleshooting

Issue	Solution
OutOfMemoryError with full model	Need 8× H200 (1128GB total). Use FP8 weights, set --gpu-memory-utilization 0.90.
GGUF inference very slow	Ensure enough RAM for the quant size. Q2_K_XL needs ~375GB RAM+VRAM combined.
Vision not working in llama.cpp	Vision support for K2.5 GGUF is not available yet — use vLLM for multimodal.
Repetitive output	Set temperature=0.6 (instant) or 1.0 (thinking). Add min_p=0.01.
Model download takes forever	~630GB FP8 checkpoint. Use huggingface-cli download with --resume-download.
Tool calls not parsed	Add --tool-call-parser kimi_k2 --enable-auto-tool-choice to vLLM serve command.

Further Reading

• Kimi K2.5 on HuggingFace

• Kimi K2.5 Tech Blog

• Kimi K2.5 Paper

• vLLM K2.5 Recipe

• Unsloth GGUF Quantizations

• Moonshot API Platform

• Kimi K2 GitHub