Instructions to use IFM/K2-Horizon-375B-A23B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IFM/K2-Horizon-375B-A23B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="IFM/K2-Horizon-375B-A23B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IFM/K2-Horizon-375B-A23B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IFM/K2-Horizon-375B-A23B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IFM/K2-Horizon-375B-A23B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IFM/K2-Horizon-375B-A23B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IFM/K2-Horizon-375B-A23B

SGLang

How to use IFM/K2-Horizon-375B-A23B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "IFM/K2-Horizon-375B-A23B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IFM/K2-Horizon-375B-A23B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "IFM/K2-Horizon-375B-A23B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IFM/K2-Horizon-375B-A23B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IFM/K2-Horizon-375B-A23B with Docker Model Runner:
```
docker model run hf.co/IFM/K2-Horizon-375B-A23B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

K2-Horizon-375B-A23B

K2-Horizon-375B-A23B is the flagship of the K2-Horizon family: a sparse Mixture-of-Experts model that stores 375B parameters and runs 23B per token, with a 512K context window. We have released the final checkpoint; intermediate checkpoints, along with the data and the training code, will be released.

K2-Horizon-375B-A23B benchmark results against open MoE, dense, and closed models

K2-Horizon-375B-A23B Highlights

Frontier-class agentic performance. On agentic tool use, terminal, and long-horizon workflow benchmarks it matches or beats open-weight MoE models up to 2.6× its size and is competitive with closed frontier models (see Benchmark Results).
512K context. Native 524,288-token context from the midtraining stages onward.
Intermediate checkpoints. Intermediate checkpoints will be released so capability changes can be studied across training rather than at a single checkpoint.
Fully open. Training data/recipe and the training code will be made public.

Benchmark Results

		Open-weight models				Closed models
	K2-Horizon-375B-A23B	Nemotron 3 Ultra	Inkling (xhigh)	MiniMax-M3	GLM 5.2 (max)	GPT 5.6 Luna (max)	GPT 5.6 Terra (high)	Claude Sonnet5 (max)
# Params	375B	550B	975B	428B	753B	--	--	--
# Activated params	23B	55B	41B	23B	40B	--	--	--
Architecture	MoE	MoE	MoE	MoE	MoE	Closed	Closed	Closed
Agents
GDPVal-AA Real-world professional tasks (Elo)	1,441	1,162	1,234	1,380	1,498	1,569	1,503	1,584
tau3-Banking Agentic tool use	34.0	14.2	29.1	15.3	34.6	31.1	28.7	37.3
Toolathlon Verified Agentic tool use	65.3	34.3	45.5	53.7	59.9	67.5	64.8	71.6
Automation Bench Public Workflow automation	25.3	8.0	12.8	20.5	26.2	33.5	28.0	34.7
Apex-Agents (pass@1) Long-horizon professional workflows	24.8	9.0	19.0	23.8	26.9	28.6	25.4	31.7
MCPMark MCP tool use	67.7	45.7	51.2	48.8	72.4	66.9	74.0	65.3
BrowseComp Deep web research	72.8	44.4	77.1	83.5	--	83.3	--	84.7
WildClawBench In-the-wild agentic tasks	50.9	34.2	52.3	56.4	55.0	50.4	60.0	--
Coding
Terminal-Bench 2.1 Agentic terminal use	70.2	53.9	55.1	65.2	77.9	80.9	75.7	80.5
SciCode Scientific coding	42.7	39.9	46.1	45.4	50.5	52.5	50.1	53.6
SWE-Atlas-QnA (strict) Repo-level code Q&A	48.4	--	25.5	42.3	46.4	--	--	--
SWE Bench Pro (strict) Software engineering	42.6	38.7	43.1	43.8	46.7	48.8	--	--
Scientific Reasoning
Humanity's Last Exam (without tools) Expert-level reasoning	32.0	28.4	31.9	39.0	41.1	39.5	38.5	41.3
GPQA Diamond Graduate-level science QA	87.3	86.7	87.2	92.9	89.5	91.1	89.6	91.1
CritPt Frontier physics reasoning	8.6	3.1	5.4	3.7	20.9	21.0	22.9	16.9
General
AA-LCR Long-context reasoning	76.0	71.0	73.3	80.3	76.7	78.3	73.3	77.0
AA-Omniscience Accuracy Factual accuracy	23.0	23.0	42.0	17.0	24.0	43.0	45.0	40.0
AA-Omniscience Non-Hallucination Non-hallucination rate	74.7	70.0	32.0	82.0	74.0	7.0	10.0	61.0

Scores in %. SWE-Atlas-QnA and SWE Bench Pro: strict = no internet. BrowseComp: different models use different harness, we use the Discard-all@95k context length proposed in DeepSeek-V3.2 technical report. WildClawBench: we use a subset of the English text-only-modality tasks. Apex-Agents: we use a subset of text-only-modality tasks. GDPVal-AA is the Elo rating.

Quickstart

Serving

vLLM, recipe at recipes.vllm.ai/IFM:

vllm serve IFM/K2-Horizon-375B-A23B \
  --revision main \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 131072 \
  --reasoning-parser k2_horizon \
  --tool-call-parser k2_horizon \
  --enable-auto-tool-choice

SGLang recipe validated on 8× H200 in the SGLang K2 Horizon cookbook:

python3 -m sglang.launch_server \
  --model-path IFM/K2-Horizon-375B-A23B \
  --revision main \
  --tp 8 \
  --ep 8 \
  --dtype bfloat16 \
  --attention-backend fa3 \
  --model-loader-extra-config '{"enable_multithread_load":false}' \
  --reasoning-parser k2_horizon \
  --tool-call-parser k2_horizon \
  --host 0.0.0.0 --port 30000

API Usage

Recommended settings: reasoning_effort="high", temperature=1.0, top_p=0.95. Reasoning depth is selected per request through chat_template_kwargs. Thinking is returned in reasoning_content and the answer in content.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="IFM/K2-Horizon-375B-A23B",
    messages=[{"role": "user", "content": "Explain the result step by step."}],
    temperature=1.0,
    top_p=0.95,
    max_tokens=32768,
    extra_body={"chat_template_kwargs": {"reasoning_effort": "high"}},
)
message = response.choices[0].message
print("Reasoning:", getattr(message, "reasoning_content", None))
print("Answer:", message.content)

Transformers

Validated with Transformers 4.57.6, PyTorch 2.13.0, Safetensors 0.8.0.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "IFM/K2-Horizon-375B-A23B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", dtype="bfloat16", low_cpu_mem_usage=True, trust_remote_code=True
)

inputs = tokenizer("Explain why long-context evaluation is difficult.", return_tensors="pt").to(model.device)
inputs.pop("token_type_ids", None)
outputs = model.generate(**inputs, max_new_tokens=32768, temperature=1.0, top_p=0.95, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Best Practices

Reasoning effort: always high. All reported results use high reasoning effort. Pass {"chat_template_kwargs": {"reasoning_effort": "high"}} on every request.
Sampling parameters. temperature=1.0, top_p=0.95.
Serving. Use the validated SGLang recipe above: BF16, TP=8 on one 8× H200 node, FlashAttention-3, with multithreaded weight loading disabled. Full recipes for every K2-Horizon size, with measured H200 latency and throughput, are in the SGLang cookbook and the vLLM recipe.
Parsers. Enable the k2_horizon reasoning parser for chat, and add the k2_horizon tool-call parser for agent use. Leave both off for plain completion-style generation.

Citation

@misc{k2horizon2026,
  title  = {Introducing K2 Horizon: Frontier Performance, Radically Open},
  author = {{IFM Team}},
  year   = {2026},
  url    = {https://ifm.ai/blog/k2/},
}

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Safetensors

Model size

379B params

Tensor type

BF16

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Quantizations

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Collection including IFM/K2-Horizon-375B-A23B

K2 Horizon

Collection

K2 Horizon models, datasets, and supporting resources • 22 items • Updated about 20 hours ago • 71