Cohere Command A+ Builder Guide: Self-Hosting on 2×H100 and Native Citations in RAG Pipelines

Cohere released Command A+ on May 20, 2026 — 218 billion parameters, 25 billion active per token, Apache 2.0, with something no other major open-weight model ships: citation generation trained into the architecture itself.

Most builders will encounter this model for the first time through a headline about “runs on 2×H100.” That’s true and it matters, but the headline buries the lead. The real shift is licensing combined with citations combined with the deployment footprint: you can now self-host a frontier-adjacent model on two commodity datacenter GPUs, with no Cohere vendor agreement, with machine-readable citation attribution on every factual claim — in a regulated industry where the previous options were “pay for enterprise API access” or “build a fragile post-processing citation layer on top of a model that wasn’t trained to do this.”

This guide covers the three decisions Command A+ changes: where to run it, how to integrate native citations, and when it actually beats the alternatives.

What’s New and Why It Changes Deployment Math

Two properties of Command A+ are genuinely novel in combination:

Apache 2.0. Cohere’s predecessor (Command A) shipped under CC-BY-NC 4.0 — meaning commercial deployment required a separate enterprise license. Apache 2.0 means you take the weights, you run them, you ship a product. No Cohere agreement required. No revenue caps. No non-compete clauses. This matters most in regulated industries (healthcare, finance, government) where procurement teams read license files.

Quantization-Aware Distillation (QAD). Cohere built QAD to preserve attention pathway precision while quantizing only the MoE expert layers, producing near-lossless W4A4 quantization. The result: 375 tokens/second output throughput on 2×H100 80GB. The same model in BF16 needs 8×H100. At W4A4, the H100 requirement drops to two — a cost difference of roughly 4× on GPU allocation.

For builders running their own inference infrastructure, the combination of Apache 2.0 and 2×H100 W4A4 makes Command A+ a plausible self-host where Command A was not.

Self-Hosting: Hardware and Software Setup

Hardware Requirements

Precision	GPU Requirement	Throughput
BF16	~8×H100 80GB	~180 t/s
FP8	~4×H100 80GB	~280 t/s
W4A4 (QAD)	2×H100 80GB or 1×B200	375 t/s

The 2×H100 headline refers to the W4A4 quantized version. If you’re running BF16 for maximum quality (e.g., fine-tuning, research), budget 8 GPUs. For production inference where the QAD quality claims hold, 2×H100 is accurate.

Model Weights

Weights are on HuggingFace under the CohereLabs organization:

BF16: CohereLabs/command-a-plus-05-2026
FP8: CohereLabs/command-a-plus-05-2026-fp8
W4A4 (QAD): Apply Cohere’s QAD quantization tooling to the BF16 or FP8 weights — quantized checkpoints are available via Cohere’s documentation

All artifacts are fully downloadable. Air-gapped deployment works once the weights are transferred.

vLLM Setup

vLLM supports Command A+ from day one. For two H100 GPUs:

pip install vllm

# Serve Command A+ W4A4 on two H100s with tensor parallel
python -m vllm.entrypoints.openai.api_server \
  --model CohereLabs/command-a-plus-05-2026 \
  --quantization w4a4 \
  --tensor-parallel-size 2 \
  --max-model-len 131072 \
  --dtype bfloat16 \
  --port 8000

The --tensor-parallel-size 2 flag splits the model across both GPUs. vLLM handles the MoE routing — you don’t need to configure anything specific for the sparse MoE architecture.

At W4A4, expect 113ms time-to-first-token and ~375 tokens/second sustained output throughput at batch size 1. Throughput scales with batching.

Docker

For containerized deployments, the standard vLLM Docker image works:

docker run --runtime nvidia --gpus all \
  -v /path/to/model-weights:/model \
  -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model /model \
  --quantization w4a4 \
  --tensor-parallel-size 2

The OpenAI-compatible API server vLLM exposes means any existing OpenAI SDK client works against self-hosted Command A+ with a single base_url change.

Native Citations: How They Work and How to Parse Them

This is the feature most worth understanding in detail before building a RAG pipeline around Command A+.

The Architecture

Most RAG citation pipelines work like this:

Retrieve relevant chunks from a vector store
Pass chunks + query to the model
Model generates an answer
A post-processing step tries to match phrases in the answer to source chunks
Citation references are appended to the output

Step 4 is where things break. Phrase matching is brittle. The model generates paraphrases. Claims blend across multiple sources. The citation attribution ends up being approximate at best, wrong at worst — not acceptable in legal, clinical, or financial contexts.

Command A+ generates citations during inference using <co> tags that wrap factual claims inline:

According to the 2025 earnings report, total revenue was
<co doc="earnings-2025-q4" row="revenue-total">$4.2 billion</co>,
representing a <co doc="earnings-2025-q4" row="yoy-growth">23% year-over-year increase</co>.

The model is trained to attribute as it generates, not after. Each <co> tag references the source document or database row that supports the wrapped claim. No post-processing required.

API Usage

When using the Cohere API (or your self-hosted vLLM endpoint with the Cohere-compatible API format), pass your source documents in the documents parameter:

import cohere

co = cohere.Client("your-api-key")
# or for self-hosted: cohere.Client("unused", base_url="http://localhost:8000")

response = co.chat(
    model="command-a-plus-05-2026",
    message="What was the revenue growth in Q4 2025?",
    documents=[
        {
            "id": "earnings-2025-q4",
            "title": "Q4 2025 Earnings Report",
            "text": "Total revenue was $4.2 billion in Q4 2025, a 23% increase year-over-year..."
        },
        {
            "id": "earnings-2025-q3",
            "title": "Q3 2025 Earnings Report",
            "text": "Total revenue was $3.8 billion in Q3 2025..."
        }
    ]
)

print(response.text)
# Output includes inline <co> tags with doc IDs

Parsing Citations

The <co> tags in the response text are machine-readable. Parse them to extract which sources support which claims:

import re
from dataclasses import dataclass

@dataclass
class Citation:
    claim: str
    doc_id: str
    row: str | None = None

def parse_citations(text: str) -> tuple[str, list[Citation]]:
    """
    Returns clean text with citations removed, plus list of Citation objects.
    """
    citations = []
    
    def extract_citation(match):
        attrs = match.group(1)
        claim_text = match.group(2)
        
        doc_match = re.search(r'doc="([^"]+)"', attrs)
        row_match = re.search(r'row="([^"]+)"', attrs)
        
        doc_id = doc_match.group(1) if doc_match else None
        row = row_match.group(1) if row_match else None
        
        if doc_id:
            citations.append(Citation(
                claim=claim_text,
                doc_id=doc_id,
                row=row
            ))
        
        return claim_text  # Return clean text without tags
    
    clean_text = re.sub(r'<co([^>]*)>(.*?)</co>', extract_citation, text)
    return clean_text, citations

# Usage
clean_text, citations = parse_citations(response.text)
for citation in citations:
    print(f"Claim: '{citation.claim}' → Source: {citation.doc_id}")

What This Replaces in Your Pipeline

Traditional RAG citation pipeline (before Command A+):

[Query] → [Retrieval] → [LLM] → [Answer] → [Phrase Matcher] → [Citation Refs]

With Command A+:

[Query] → [Retrieval] → [Command A+] → [Answer with inline citations]

The phrase matcher step disappears. The citation attribution is exact — it comes from the model, not from string matching. For legal, clinical, or financial output where unsourced claims are a compliance liability, this changes the trust model of your pipeline output.

RAG Pipeline Architecture for Regulated Industries

Here’s a complete pattern for building a citation-verified RAG pipeline on self-hosted Command A+:

from cohere import Client
from your_vector_store import retrieve_chunks

# Point at self-hosted vLLM endpoint
co = Client("unused", base_url="http://your-inference-server:8000/v1")

def regulated_rag_query(query: str, source_corpus: list[dict]) -> dict:
    """
    Returns answer with machine-readable citations for compliance workflows.
    
    source_corpus: list of {"id": str, "title": str, "text": str}
    """
    # Retrieve relevant chunks from your vector store
    relevant_docs = retrieve_chunks(query, source_corpus, top_k=8)
    
    response = co.chat(
        model="command-a-plus-05-2026",
        message=query,
        documents=relevant_docs,
        # Cohere API: enable grounded generation mode
        connectors=[],  # No live connectors; use documents parameter only
    )
    
    clean_text, citations = parse_citations(response.text)
    
    return {
        "answer": clean_text,
        "citations": [
            {
                "claim": c.claim,
                "source_id": c.doc_id,
                "source_title": next(
                    (d["title"] for d in relevant_docs if d["id"] == c.doc_id),
                    "Unknown"
                ),
            }
            for c in citations
        ],
        "uncited_claims": _find_uncited_claims(clean_text, citations),
    }

def _find_uncited_claims(text: str, citations: list[Citation]) -> list[str]:
    """
    Identify sentences in the response that carry no citation.
    Used for compliance review flagging.
    """
    cited_claims = {c.claim for c in citations}
    sentences = text.split(". ")
    return [s for s in sentences if s not in cited_claims and len(s) > 20]

The uncited_claims check is useful in compliance contexts: any factual-sounding sentence that Command A+ didn’t cite is flagged for human review rather than silently passing through.

When to Use Command A+ vs. Alternatives

The Intelligence Index score of 37 (Artificial Analysis composite) places Command A+ below the 57-60 frontier tier. This is not a general-purpose frontier model. The choice of when to use it versus alternatives is specific.

Use Command A+ when:

You need data sovereignty — weights on your hardware, no data leaves your network. Apache 2.0 enables this without any vendor conversation.
Your pipeline requires machine-readable citation attribution on factual claims. No other open-weight model at this scale has architecture-level citations.
You’re in a regulated industry (legal, clinical, financial, government) where the citation and sovereignty combination are table stakes.
You’re building multilingual enterprise applications — 48 language support is broader than most alternatives at this tier.
You need document and spreadsheet reasoning — the multimodal capability is focused here and the benchmark numbers are competitive.

Use alternatives when:

Coding tasks — Cohere explicitly recommends GPT-5.5 or Claude Opus 4.7 for code. Command A+ is not competitive on coding benchmarks.
Graduate-level scientific reasoning — HLE at 11% is a significant gap. If your pipeline needs deep scientific reasoning, DeepSeek V4 or frontier APIs are better.
Cost-sensitive non-regulated deployments — DeepSeek V4 ($0.27/$1.10 per M tokens, MIT license) is dramatically cheaper and more capable on raw reasoning. If you don’t need citations or specific sovereignty requirements, DeepSeek V4 is harder to beat on cost/performance.
Current events or knowledge recency — April 2025 knowledge cutoff is 13 months old at launch. Use a model with a more recent cutoff if recency matters.

API vs. self-host decision:

At $2.50 input / $10.00 output per M tokens via the Cohere API, the crossover point for self-hosting typically falls around 5-10 million output tokens per month depending on your H100 amortization rate. Below that volume, API is cheaper. Above it, two H100s at W4A4 typically pay for themselves within 60-90 days of production load.

Azure AI Foundry

If you’re in a Microsoft enterprise environment, Command A+ is available on Azure AI Foundry without managing your own GPU infrastructure. This captures the Apache 2.0 legal clarity and the native citations while Microsoft handles the H100 allocation.

The tradeoff: data leaves your datacenter to Microsoft’s Azure region. For teams with Azure data residency agreements, this can satisfy regulated industry requirements. For teams requiring true air-gap deployment, self-hosting is still necessary.

The Builder Decision

Command A+ is a narrow-fit model with a genuine unlock for that niche. The Apache 2.0 licensing and native citation architecture together address a specific set of builder problems that no other open-weight model at this scale handles.

If you’re building a general-purpose chatbot, a coding tool, or a reasoning pipeline, this is not the model. If you’re building a RAG pipeline that needs to output compliant, auditable, machine-readable citations for a regulated industry — and you need to run it on your own hardware without a vendor agreement — Command A+ is the clearest option in the open-weight landscape right now.

Three action items if you’re evaluating it:

Check the CohereLabs/command-a-plus-05-2026 weights on HuggingFace against your inference hardware budget. The W4A4 2×H100 claim is real.
Build a citation parsing prototype with the <co> tag parser above before committing to the architecture. Verify citation accuracy on a representative sample of your actual documents.
Run the compliance check: read the Apache 2.0 license, compare to CC-BY-NC 4.0 (the old Cohere terms), and confirm with your legal team that the license meets your deployment requirements. The license is the feature for regulated industry procurement.

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