Llama 4 Behemoth: which providers host it today

Tracking Llama 4 Behemoth provider availability is the first step before you wire a model in this weight class into production. The release shipped with a gated commercial license, so not every inference platform that hosted previous Llama generations will have it on day one. Below is the concrete landscape as it stands, with the integration details that matter when you’re writing client code.

1. Meta’s Official Distribution and Self-Host

Meta retains primary control of the weights. Access typically requires a request form and a Hugging Face mirror approved under the license. For Behemoth, expect the same pattern: downloadable shards for teams that want to serve in their own VPC, plus a managed API reserved for approved enterprise tiers.

Self-hosting demands a serving stack like vLLM or TensorRT-LLM with tensor parallelism across many GPUs. Most product teams won’t run this themselves; they’ll call a hosted provider. If you do, the launch command is unchanged from prior Llama generations.

huggingface-cli download meta-llama/Llama-4-Behemoth \
  --token $HF_TOKEN --local-dir ./behemoth

2. Hugging Face Inference Endpoints

Hugging Face is the canonical mirror for open-weight models. For Llama 4 Behemoth provider availability, HF hosts the weights and offers paid Inference Endpoints on accelerator instances. You deploy a private endpoint with a few clicks; the OpenAI-compatible router is built in.

Billing is per minute of provisioned hardware, not per token, which makes cost modeling different from API metros. Latency scales with tensor-parallel degree—expect TP=8 across eight GPUs for this model.

from openai import OpenAI

client = OpenAI(
    base_url="https://your-endpoint.huggingface.cloud/v1",
    api_key="hf_...",
)
client.chat.completions.create(
    model="meta-llama/Llama-4-Behemoth",
    messages=[{"role": "user", "content": "Summarize RFC 9000"}],
)

3. Together AI

Together has historically been first to host new Llama weights with optimized inference kernels. They usually provide a serverless endpoint and a dedicated instance option. For Behemoth, expect a meta-llama-4-behemoth model slug on their OpenAI-compatible API.

Their routing handles speculative decoding, so time-to-first-token is competitive. Rate limits are generous on the dedicated tier. Use the stream flag for long outputs to avoid gateway timeouts.

{
  "model": "meta-llama-4-behemoth",
  "messages": [{"role": "user", "content": "Write a SQL migration"}],
  "max_tokens": 1024,
  "stream": true
}

4. Fireworks AI

Fireworks focuses on low-latency serving via custom attention kernels. They typically onboard Llama releases within days. The model ID follows accounts/fireworks/models/llama-4-behemoth. They support function calling and structured outputs, which matters if you’re building agents.

One caveat: Behemoth’s size means Fireworks may only offer it on a waitlist initially. Check the model page for availability: public vs private before you hardcode the slug.

const res = await fetch("https://api.fireworks.ai/inference/v1/chat/completions", {
  method: "POST",
  headers: { "Authorization": `Bearer ${FW_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({ model: "accounts/fireworks/models/llama-4-behemoth", messages: [{role:"user",content:"ping"}] })
});

5. Groq

Groq’s LPUs deliver extreme token throughput but require model compilation. For Llama 4 Behemoth provider availability, Groq may lag because the model must fit on their memory fabric. If they host it, it’s likely via a quantized variant to fit on their cards.

Assume the endpoint is OpenAI-compatible at api.groq.com/openai/v1. The model field will be llama-4-behemoth-groq. Throughput will be best-in-class; cost per token may be higher due to specialized hardware.

client = OpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.environ["GROQ_KEY"])
client.chat.completions.create(model="llama-4-behemoth-groq", messages=[{"role":"user","content":"Explain MoE"}])

6. Replicate

Replicate packages models as Cog containers. Community publishers often push Llama weights same day; official verified entries appear later. For Behemoth, you’ll likely see a community model first, with a verified one after license clearance.

Replicate’s API is not strictly OpenAI-compatible; it uses a prediction poll pattern. That complicates drop-in replacement behind a unified client.

curl -s -X POST https://api.replicate.com/v1/predictions \
 -H "Authorization: Bearer $REP_KEY" \
 -d '{"version":"abc123","input":{"prompt":"Hello"}}'

7. AWS Bedrock

Bedrock integrates third-party models with IAM auth. Meta models appear as meta.llama4-behemoth-*. Availability is region-scoped; us-east-1 typically leads. You call via the Bedrock Runtime Converse API, not the OpenAI spec, so you need the boto3 client.

import boto3
bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")
bedrock.converse(modelId="meta.llama4-behemoth-1", messages=[{"role":"user","content":[{"text":"Hi"}]}])

8. Azure AI Studio

Microsoft usually secures early access for large Llama variants on Azure AI Studio. The model deploys as a managed online endpoint with ARM templates. You get Entra ID integration and private networking. The native API is REST, though an OpenAI-compatible proxy is sometimes available via extensions.

{ "input": { "messages": [{"role":"user","content":"Status?"}] }, "params": {"max_new_tokens":200} }

9. Google Vertex AI

Vertex hosts open models in its Model Garden. Llama 4 Behemoth provider availability here depends on Google’s publishing pipeline; often it’s a click-to-deploy endpoint with a container. Inference is billed on TPUs, which may be cheaper than GPUs for this size.

gcloud ai endpoints deploy-model $ENDPOINT_ID --model=$MODEL_ID --region=us-central1

10. Aggregators: OpenRouter and n4n.ai

Aggregators abstract over the above. OpenRouter lists each provider’s Behemoth endpoint under a single slug. n4n.ai provides one OpenAI-compatible endpoint that addresses 240+ models and automatically falls back when a provider is rate-limited or degraded, which is useful when Llama 4 Behemoth provider availability is spotty at launch.

You send a routing directive and the gateway forwards provider cache-control hints. This means you can write one client and not rewrite when a backend goes down.

from openai import OpenAI
client = OpenAI(base_url="https://api.n4n.ai/v1", api_key="sk-...")
client.chat.completions.create(
    model="meta-llama/llama-4-behemoth",
    messages=[{"role":"user","content":"Route me"}],
    extra_headers={"x-provider-pref":"together,fireworks"}
)

Synthesis

The practical takeaway: Llama 4 Behemoth provider availability is fragmented at launch. Hyperscalers (AWS, Azure, Vertex) require platform-specific SDKs; OpenAI-compatible hosts (Together, Fireworks, HF, Groq) let you reuse one client; aggregators collapse the difference. If you need resilience, put a gateway in front and encode provider preferences in headers.