# Computer Use Agents Go Local: A Deep Technical Dive into On-Device GUI Automation, Quantized Inference & Holo3.1

> Source: <https://dev.to/monuminu/computer-use-agents-go-local-a-deep-technical-dive-into-on-device-gui-automation-quantized-2m3g>
> Published: 2026-06-03 04:48:07+00:00

Meta Description:Learn how to build production-grade local computer use agents using Holo3.1's quantized model family (FP8/NVFP4/GGUF). Deep dive into quantization tradeoffs, the perceive-decide-act loop, Python code examples, and multi-agent orchestration patterns — with zero data leaving your machine.

*Published: June 3, 2026 · 14 min read*

What if your AI agent never sent a single byte to the cloud?

Every screenshot it captures, every form it fills, every file it reads — all processed locally, on your hardware, under your control. No API keys. No data-in-transit risk. No per-token billing shock at the end of the month.

For two years, computer use agents — AI systems that operate GUIs like a human would — have been almost exclusively a cloud-first technology. OpenAI's Operator, Anthropic's Computer Use, and early Holo3 all required round-trips to remote inference endpoints. You'd snap a screenshot, ship it over HTTPS, wait hundreds of milliseconds for a decision, then execute the action. Functional, but fundamentally leaky.

That changed on **June 2, 2026**, when Hcompany released **Holo3.1**: the first production-grade computer use model family to ship quantized checkpoints — FP8, NVFP4, and Q4 GGUF — purpose-built for fully local inference. Simultaneously, the developer internet was buzzing with a 716-upvote Hacker News post proving that a 2016 Intel Xeon with no GPU could run Gemma 4 at acceptable speeds using aggressive speculative decoding. The message was unmistakable: **the local inference era for agentic AI has arrived.**

In this post, we'll go deep — from the architecture of Holo3.1 and the mathematics of quantization, through to fully working Python code that builds a local computer use agent loop from scratch, and finally to production deployment patterns for teams shipping these systems at scale.

*The perceive-decide-act loop running entirely on-device. No data leaves the machine.*

A **computer use agent (CUA)** is an AI system that controls a computer's graphical interface — the same way a human does — by interpreting screenshots and emitting discrete GUI actions. Unlike chat-based agents that call APIs or manipulate text, CUAs operate at the **pixel and interaction layer**: they see what's on screen and decide where to click, what to type, when to scroll, and how to navigate.

A typical CUA action space includes:

| Action Type | Example |
|---|---|
`click(x, y)` |
Left-click at pixel coordinates |
`double_click(x, y)` |
Double-click to open a file |
`type(text)` |
Keyboard input into focused element |
`scroll(x, y, direction, amount)` |
Scroll in any direction |
`key(combo)` |
Keyboard shortcuts (e.g., `Ctrl+C` ) |
`screenshot()` |
Capture current screen state |
`drag(x1, y1, x2, y2)` |
Click-drag for selections or UI moves |

The agent perceives the world exclusively through **screenshots** (or, in more advanced setups, accessibility tree data), reasons about the current state vs. the goal, and emits the next best action. This loop continues until the task is complete or a termination condition is reached.

CUAs are compelling for automating workflows that have **no API** — legacy enterprise software, internal web apps, desktop tools that predate REST, and mobile applications. They're the ultimate last-resort automation layer. More practically, they can now act as the *hands* of a larger agentic system: an orchestrator LLM can delegate "book the flight" to a CUA without caring that the airline website doesn't expose a machine-readable endpoint.

Before we dive into Holo3.1 specifically, it's worth grounding why **local inference** matters so dramatically for computer use agents versus, say, a text summarization task.

*Cloud inference introduces latency, cost, and privacy exposure at every agent step.*

When a CUA takes a screenshot to send to a cloud API, it captures everything visible on screen: email contents, financial data, proprietary code, patient information, internal tools, authentication tokens visible in browser address bars. Every API call is a potential compliance violation. For enterprise deployments under HIPAA, SOC 2, GDPR, or internal data classification policies, this is a blocker — not a concern.

Local inference eliminates the exfiltration surface entirely.

A cloud-hosted CUA with ~450ms round-trip latency executing a 20-step task accumulates **9 full seconds of pure network wait time** — before accounting for inference time. Local inference on a quantized model can cut step time from 6.8 seconds (FP8 on DGX Spark) to 3.3 seconds (NVFP4 on DGX Spark) — a **~2× end-to-end speedup** demonstrated in Holo3.1's own benchmarks. For interactive workflows, this is the difference between a tool that feels snappy and one that feels broken.

Consider a workflow executing 1,000 agent steps per day. At a hypothetical $0.01/step cloud API cost, that's $10/day or $3,650/year — just for inference. A local DGX Spark (or even a gaming GPU) amortizes its cost across unlimited inference. The break-even point arrives faster than most teams expect, especially once multi-agent workflows start multiplying step counts.

Local inference isn't free of engineering challenges. The fundamental constraint for LLM inference on any hardware is **memory bandwidth**, not compute throughput. During token generation (the "decoding pass"), the processor must stream the entire model's weights from RAM into cache for every single token emitted.

On a 2016 Xeon with DDR3 RAM — as demonstrated in the viral Hacker News post — this bottleneck is severe. The CPU is often idle, waiting for data to arrive over the memory bus. The same constraint applies on GPUs (HBM bandwidth limits) and Apple Silicon (unified memory limits). Every quantization format, every inference optimization, and every speculative decoding technique ultimately aims to defeat this memory wall.

Holo3.1 is built on the **Qwen family** of base models and fine-tuned with a mixture of web interaction data, desktop automation traces, mobile UI trajectories, and synthetic function-calling demonstrations. The training objective is to maximize task completion rate across three production axes: **environment robustness**, **framework interoperability**, and **deployment flexibility**.

| Model | Parameters | Best For |
|---|---|---|
| Holo3.1-0.8B | 0.8B | Ultra-lightweight edge agents |
| Holo3.1-4B | 4B | Cost-efficient cloud/local deployment |
| Holo3.1-9B | 9B | Balanced performance and step latency |
| Holo3.1-35B-A3B | 35B total / 3B active (MoE) | State-of-the-art task completion |

The `35B-A3B`

suffix on the flagship model signals its **Mixture-of-Experts (MoE)** architecture: 35 billion total parameters with only ~3 billion active per forward pass. This dramatically reduces per-token compute cost while retaining the capacity of a much larger dense model — a design pattern that has proven critical for making large models viable at inference time.

The single biggest complaint from Holo3 production deployments was **distribution shift**: strong benchmark performance that failed to transfer when the model ran inside a third-party agent harness, on a different OS, or against a mobile UI. Holo3.1 addresses this through three targeted improvements:

**1. Cross-Harness Function Calling**

Holo3.1 now natively supports OpenAI-compatible function-calling protocol in addition to its structured JSON action outputs. This means the same model can plug into LangGraph, CrewAI, AutoGen, or a custom harness without adapter layers. On Holo3.1's internal benchmark suite, function-calling and native JSON execution now achieve **near-parity performance**, eliminating the 10–15% gap that plagued Holo3 in third-party integrations.

**2. Mobile GUI Support**

Holo3.1 was trained on Android UI traces, pushing AndroidWorld benchmark scores from 67% to **79.3%** on the 35B model and from 58% to **72%** on the 4B model. Mobile automation — navigating apps, filling forms, handling touch targets — is now a first-class capability rather than a side effect.

**3. Quantized Checkpoints for Local Deployment**

For the first time, Hcompany ships quantized weights alongside the full-precision model. This is the key to local inference viability, and we'll spend the next section unpacking what each format means technically.

Quantization is the process of reducing the numerical precision of model weights (and optionally activations) to use fewer bits per value. Lower bits = smaller model = less data to move across the memory bus per token = faster inference.

*FP8, NVFP4, and Q4-GGUF offer different precision/throughput/memory tradeoffs. Choose based on your hardware and latency requirements.*

FP8 uses 8 bits per weight value in a floating-point format (either E4M3 or E5M2). Compared to the standard BF16 (16-bit brain float), FP8 halves model memory footprint while maintaining most of the dynamic range. On NVIDIA H100/H200 and Ada Lovelace GPUs, FP8 is hardware-accelerated via dedicated tensor core units.

For Holo3.1's 35B-A3B model, FP8 achieves **the same OSWorld score** as full BF16 — meaning the quantization error is within benchmark noise. It's the safest starting point for any GPU-equipped deployment.

NVFP4 uses NVIDIA's Model Optimizer in a **W4A16 configuration**: weights stored at 4-bit precision, activations computed at 16-bit. This is distinct from naive INT4 quantization — NVFP4 uses a floating-point 4-bit format (FP4) that better preserves the weight distribution tails critical for instruction following.

The results are striking: NVFP4 delivers **1.41× the total token throughput of FP8** and **1.74× that of BF16** on DGX Spark hardware, while sitting only ~2 OSWorld benchmark points below BF16. The agent step time drops from 6.8s (FP8) to 3.3s (NVFP4) — measured end-to-end including screenshot processing and action execution. This is the format to use if you have NVIDIA Blackwell or Ada architecture hardware and care about throughput.

GGUF (the successor to GGML) is the quantization format powering `llama.cpp`

and its derivatives (`ik_llama.cpp`

, `ollama`

). Q4 quantization uses 4 bits per weight with block-wise quantization scales, making it compatible with CPU inference, Apple Silicon, and modest consumer GPUs.

The GGUF checkpoints for Holo3.1 are aimed at **consumer hardware deployment** — running the agent locally on a MacBook Pro, a Windows gaming PC, or a developer laptop. The throughput is lower than FP8/NVFP4 on server hardware, but the total cost of deployment is near-zero beyond the hardware you already own.

| Scenario | Recommended Format |
|---|---|
| NVIDIA H100/H200/B100 server | NVFP4 (W4A16) |
| NVIDIA RTX 40/50 series | FP8 |
| Apple Silicon (M3/M4 Pro/Max) | Q4 GGUF |
| Consumer NVIDIA GPU (RTX 3090/4090) | Q4 GGUF or FP8 |
| CPU-only (high RAM) | Q4 GGUF |
| Lowest latency, highest throughput | NVFP4 |

Let's get practical. The following setup targets a machine with either an NVIDIA GPU (for GGUF via llama.cpp) or Apple Silicon. We'll use the `Holo3.1-9B`

model as a good balance of performance and resource requirements.

`ffmpeg`

for screen capture on Linux

```
# Install dependencies
pip install \
  huggingface_hub \
  llama-cpp-python \
  pillow \
  pyautogui \
  openai \
  langgraph \
  mss \
  anthropic

# On macOS, also install:
brew install ffmpeg

# On Linux:
sudo apt-get install -y scrot xdotool
python
from huggingface_hub import hf_hub_download
import os

# Download Holo3.1-9B Q4_K_M GGUF (best quality/size tradeoff for 9B)
model_path = hf_hub_download(
    repo_id="Hcompany/Holo3.1-9B-GGUF",
    filename="holo3.1-9b-q4_k_m.gguf",
    local_dir="./models",
    local_dir_use_symlinks=False,
)

print(f"Model downloaded to: {model_path}")
# Expected: ./models/holo3.1-9b-q4_k_m.gguf (~5.5GB)
python
from llama_cpp import Llama

llm = Llama(
    model_path="./models/holo3.1-9b-q4_k_m.gguf",
    n_ctx=8192,           # context window
    n_gpu_layers=-1,      # offload all layers to GPU (-1 = auto)
    n_threads=8,          # CPU threads for non-GPU layers
    verbose=False,
    # Enable flash attention for efficiency
    flash_attn=True,
    # Use memory-mapped loading to reduce RAM pressure
    use_mmap=True,
    use_mlock=True,       # prevent model from being swapped to disk
)

print("Model loaded successfully.")
print(f"Context size: {llm.n_ctx()} tokens")
```

The core of any computer use agent is the **perceive-decide-act cycle**. Here's a full, runnable implementation:

``` python
import base64
import json
import time
from io import BytesIO
from typing import Optional

import mss
import mss.tools
import pyautogui
from PIL import Image
from llama_cpp import Llama

# ── Action execution helpers ──────────────────────────────────────────────────

def execute_action(action: dict) -> str:
    """Execute a GUI action returned by the model."""
    action_type = action.get("type")

    if action_type == "click":
        x, y = action["x"], action["y"]
        pyautogui.click(x, y)
        return f"Clicked at ({x}, {y})"

    elif action_type == "double_click":
        x, y = action["x"], action["y"]
        pyautogui.doubleClick(x, y)
        return f"Double-clicked at ({x}, {y})"

    elif action_type == "type":
        text = action["text"]
        pyautogui.typewrite(text, interval=0.05)
        return f"Typed: {text[:50]}..."

    elif action_type == "key":
        combo = action["combo"]
        pyautogui.hotkey(*combo.split("+"))
        return f"Pressed key combo: {combo}"

    elif action_type == "scroll":
        x, y = action["x"], action["y"]
        direction = action.get("direction", "down")
        amount = action.get("amount", 3)
        pyautogui.scroll(amount if direction == "up" else -amount, x=x, y=y)
        return f"Scrolled {direction} at ({x}, {y})"

    elif action_type == "screenshot":
        return "Taking screenshot..."  # handled in main loop

    elif action_type == "done":
        return "TASK_COMPLETE"

    else:
        return f"Unknown action type: {action_type}"

# ── Screenshot capture ────────────────────────────────────────────────────────

def capture_screenshot(monitor_index: int = 1) -> str:
    """Capture screen and return as base64-encoded JPEG string."""
    with mss.mss() as sct:
        monitor = sct.monitors[monitor_index]
        screenshot = sct.grab(monitor)
        img = Image.frombytes("RGB", screenshot.size, screenshot.bgra, "raw", "BGRX")
        # Resize to 1280x800 to reduce token count (vision models are pixel-hungry)
        img = img.resize((1280, 800), Image.LANCZOS)
        buffer = BytesIO()
        img.save(buffer, format="JPEG", quality=85)
        return base64.b64encode(buffer.getvalue()).decode("utf-8")

# ── Agent system prompt ───────────────────────────────────────────────────────

SYSTEM_PROMPT = """You are a computer use agent. You observe screenshots of a computer screen
and output JSON actions to accomplish the user's task.

Always respond with a JSON object in this exact format:
{
  "reasoning": "brief explanation of what you see and what action to take",
  "action": {
    "type": "click|double_click|type|key|scroll|screenshot|done",
    ... (action-specific fields)
  }
}

For click/double_click: include "x" and "y" (pixel coordinates).
For type: include "text".
For key: include "combo" (e.g., "ctrl+c", "enter").
For scroll: include "x", "y", "direction" ("up"/"down"), "amount" (integer).
For done: include "result" with a summary of what was accomplished.

Be precise with coordinates. When the task is complete, use action type "done".
"""

# ── Main agent loop ───────────────────────────────────────────────────────────

def run_computer_use_agent(
    llm: Llama,
    task: str,
    max_steps: int = 25,
    step_delay: float = 1.0,
) -> str:
    """
    Run a computer use agent loop until task completion or max_steps.

    Args:
        llm: Loaded Llama model instance
        task: Natural language description of the task to complete
        max_steps: Maximum number of action steps before giving up
        step_delay: Seconds to wait after each action (for UI to settle)

    Returns:
        Final result string from the agent
    """
    print(f"\n🤖 Starting agent for task: {task}")
    history = []

    for step in range(1, max_steps + 1):
        print(f"\n── Step {step}/{max_steps} ──")

        # 1. PERCEIVE: Capture current screen state
        screenshot_b64 = capture_screenshot()
        print("📸 Screenshot captured")

        # 2. BUILD PROMPT: Include task, history summary, and current screenshot
        history_summary = ""
        if history:
            last_3 = history[-3:]  # keep last 3 for context without blowing budget
            history_summary = "\n".join(
                [f"Step {h['step']}: {h['reasoning']} → {h['action_type']}" for h in last_3]
            )
            history_summary = f"\n\nRecent actions:\n{history_summary}"

        user_message = f"Task: {task}{history_summary}\n\nCurrent screenshot attached. What action should I take next?"

        # 3. DECIDE: Call the local model with vision input
        # Note: llama-cpp-python supports multimodal via llava-style image embedding
        response = llm.create_chat_completion(
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": user_message},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{screenshot_b64}"
                            },
                        },
                    ],
                },
            ],
            max_tokens=512,
            temperature=0.1,  # low temp for deterministic action selection
            response_format={"type": "json_object"},
        )

        raw_output = response["choices"][0]["message"]["content"]
        print(f"🧠 Model output: {raw_output[:200]}...")

        # 4. PARSE: Extract action from JSON response
        try:
            parsed = json.loads(raw_output)
            reasoning = parsed.get("reasoning", "")
            action = parsed.get("action", {})
        except json.JSONDecodeError as e:
            print(f"⚠️  JSON parse error: {e}. Retrying step.")
            continue

        print(f"💭 Reasoning: {reasoning}")
        print(f"⚡ Action: {action}")

        # 5. RECORD: Add to history
        history.append({
            "step": step,
            "reasoning": reasoning,
            "action_type": action.get("type", "unknown"),
        })

        # 6. ACT: Execute the action
        result = execute_action(action)
        print(f"✅ Result: {result}")

        if result == "TASK_COMPLETE":
            final_result = action.get("result", "Task completed successfully.")
            print(f"\n�� Task complete! Result: {final_result}")
            return final_result

        # Wait for UI to settle after action
        time.sleep(step_delay)

    return f"Max steps ({max_steps}) reached without task completion."

# ── Entry point ───────────────────────────────────────────────────────────────

if __name__ == "__main__":
    # Load model
    llm = Llama(
        model_path="./models/holo3.1-9b-q4_k_m.gguf",
        n_ctx=8192,
        n_gpu_layers=-1,
        flash_attn=True,
        use_mmap=True,
        use_mlock=True,
        verbose=False,
    )

    # Run a task
    result = run_computer_use_agent(
        llm=llm,
        task="Open Firefox, navigate to github.com, and search for 'holo3.1'",
        max_steps=20,
        step_delay=1.5,
    )
    print(f"\nFinal result: {result}")
```

For production use, you'll want to integrate your computer use agent into a broader orchestration framework. Holo3.1's support for the **OpenAI-compatible function-calling protocol** makes this straightforward.

*A hierarchical multi-agent system: an orchestrator delegates GUI tasks to a specialist CUA, which operates tools on its behalf.*

LangGraph lets you build stateful multi-agent workflows as directed graphs. Here's how to wire a Holo3.1-powered computer use node into a LangGraph workflow:

``` python
from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, END
from langgraph.graph.message import add_messages
from langchain_core.messages import HumanMessage, AIMessage
import operator

# ── State definition ──────────────────────────────────────────────────────────

class AgentState(TypedDict):
    task: str
    messages: Annotated[list, add_messages]
    step_count: int
    completed: bool
    final_result: str

# ── Tool definitions for function-calling protocol ────────────────────────────

COMPUTER_USE_TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "click",
            "description": "Click at the specified pixel coordinates on screen",
            "parameters": {
                "type": "object",
                "properties": {
                    "x": {"type": "integer", "description": "X pixel coordinate"},
                    "y": {"type": "integer", "description": "Y pixel coordinate"},
                },
                "required": ["x", "y"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "type_text",
            "description": "Type text into the currently focused element",
            "parameters": {
                "type": "object",
                "properties": {
                    "text": {"type": "string", "description": "Text to type"},
                },
                "required": ["text"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "press_key",
            "description": "Press a keyboard key or key combination",
            "parameters": {
                "type": "object",
                "properties": {
                    "combo": {"type": "string", "description": "Key combo e.g. 'enter', 'ctrl+c'"},
                },
                "required": ["combo"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "task_complete",
            "description": "Signal that the task has been completed",
            "parameters": {
                "type": "object",
                "properties": {
                    "result": {"type": "string", "description": "Summary of what was accomplished"},
                },
                "required": ["result"],
            },
        },
    },
]

# ── CUA node ─────────────────────────────────────────────────────────────────

def computer_use_node(state: AgentState) -> AgentState:
    """LangGraph node that runs one step of the computer use agent."""
    from openai import OpenAI  # Using OpenAI-compatible local server

    # Point to local llama.cpp server (start with: llama-server -m model.gguf --port 8080)
    client = OpenAI(
        base_url="http://localhost:8080/v1",
        api_key="not-needed",  # local server doesn't require auth
    )

    screenshot_b64 = capture_screenshot()

    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {
            "role": "user",
            "content": [
                {"type": "text", "text": f"Task: {state['task']}"},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{screenshot_b64}"},
                },
            ],
        },
    ]

    # Use function-calling protocol (new in Holo3.1)
    response = client.chat.completions.create(
        model="holo3.1-9b",  # model name as configured in llama-server
        messages=messages,
        tools=COMPUTER_USE_TOOLS,
        tool_choice="auto",
        max_tokens=512,
        temperature=0.1,
    )

    tool_calls = response.choices[0].message.tool_calls

    if not tool_calls:
        return {**state, "step_count": state["step_count"] + 1}

    # Execute first tool call
    tool_call = tool_calls[0]
    fn_name = tool_call.function.name
    fn_args = json.loads(tool_call.function.arguments)

    if fn_name == "task_complete":
        return {
            **state,
            "completed": True,
            "final_result": fn_args.get("result", "Completed"),
            "step_count": state["step_count"] + 1,
        }

    # Execute the action
    action = {"type": fn_name.replace("_text", "").replace("press_key", "key"), **fn_args}
    execute_action(action)
    time.sleep(1.0)

    return {**state, "step_count": state["step_count"] + 1}

def should_continue(state: AgentState) -> str:
    """Router: continue agent loop or end."""
    if state["completed"]:
        return "end"
    if state["step_count"] >= 25:
        return "end"
    return "continue"

# ── Build the graph ───────────────────────────────────────────────────────────

def build_computer_use_graph() -> StateGraph:
    graph = StateGraph(AgentState)
    graph.add_node("computer_use", computer_use_node)
    graph.set_entry_point("computer_use")
    graph.add_conditional_edges(
        "computer_use",
        should_continue,
        {"continue": "computer_use", "end": END},
    )
    return graph.compile()

# Run it
app = build_computer_use_graph()
final_state = app.invoke({
    "task": "Find the latest Python release on python.org and copy its version number",
    "messages": [],
    "step_count": 0,
    "completed": False,
    "final_result": "",
})
print(f"Result: {final_state['final_result']}")
```

Before shipping a CUA to production, you need a rigorous evaluation harness. The two canonical benchmarks for computer use agents are **OSWorld** (desktop tasks) and **AndroidWorld** (mobile tasks).

OSWorld provides 369 computer tasks across real desktop applications (LibreOffice, GIMP, VS Code, Chrome, etc.) and scores agents on **task success rate** — did the agent complete the task as specified, verified by automated state checking?

``` python
# Minimal OSWorld evaluation harness
import subprocess
import json
from pathlib import Path

def run_osworld_task(task_config: dict, agent_fn) -> dict:
    """
    Run a single OSWorld task and return pass/fail with metadata.

    task_config example:
    {
        "task_id": "libreoffice_writer_001",
        "instruction": "Bold the first sentence of the document",
        "app": "libreoffice_writer",
        "verifier": "check_bold_first_sentence"
    }
    """
    task_id = task_config["task_id"]
    instruction = task_config["instruction"]

    # Launch the task environment (OSWorld uses VMs/containers per task)
    subprocess.run(["osworld-env", "reset", task_id], check=True)
    time.sleep(2)  # let UI settle

    # Run agent
    result = agent_fn(task=instruction, max_steps=15)

    # Run verifier
    verifier_output = subprocess.run(
        ["osworld-verify", task_id],
        capture_output=True, text=True
    )
    passed = "PASS" in verifier_output.stdout

    return {
        "task_id": task_id,
        "passed": passed,
        "agent_result": result,
        "verifier_output": verifier_output.stdout,
    }

def evaluate_agent(task_configs: list, agent_fn, output_path: str = "results.json"):
    """Run full evaluation suite and compute aggregate metrics."""
    results = []
    for cfg in task_configs:
        print(f"Running task: {cfg['task_id']}")
        result = run_osworld_task(cfg, agent_fn)
        results.append(result)
        print(f"  {'✅ PASS' if result['passed'] else '❌ FAIL'}")

    # Compute metrics
    total = len(results)
    passed = sum(1 for r in results if r["passed"])
    success_rate = passed / total * 100

    summary = {
        "total_tasks": total,
        "passed": passed,
        "success_rate": f"{success_rate:.1f}%",
        "results": results,
    }

    Path(output_path).write_text(json.dumps(summary, indent=2))
    print(f"\nOverall success rate: {success_rate:.1f}% ({passed}/{total})")
    return summary
```

For reference: Holo3.1-35B-A3B scores **~72%** on OSWorld in the function-calling harness configuration, up from ~60% for Holo3 in the same setup.

Run `llama-server`

as a persistent background process, treat it as a local OpenAI-compatible endpoint:

```
# Start llama.cpp server with Holo3.1-9B
llama-server \
  --model ./models/holo3.1-9b-q4_k_m.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  --ctx-size 8192 \
  --n-gpu-layers -1 \
  --flash-attn \
  --parallel 4 \        # handle 4 concurrent agent requests
  --mlock               # lock model in RAM
```

Then in your agent code, point `openai.base_url`

to `http://localhost:8080/v1`

.

For teams running multiple parallel agents, vLLM with NVFP4 quantization is the gold standard:

```
# vLLM with Holo3.1-35B-A3B using NVFP4 quantization
vllm serve Hcompany/Holo3.1-35B-A3B-NVFP4 \
  --quantization nvfp4 \
  --max-model-len 32768 \
  --tensor-parallel-size 2 \  # for multi-GPU setup
  --gpu-memory-utilization 0.90 \
  --enable-prefix-caching    # helps for repetitive system prompts
```

This configuration, benchmarked on DGX Spark, delivers the **3.3 second average step time** — down from 6.8s with FP8.

A highly effective pattern emerging from the community: use a large orchestrator model to plan and evaluate, while smaller specialist models handle execution:

```
# Orchestrator delegates GUI tasks to CUA specialist
orchestrator_client = OpenAI(api_key="your-key")  # cloud or local large model
cua_client = OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")  # local Holo3.1

def orchestrated_workflow(high_level_goal: str):
    # Step 1: Orchestrator decomposes goal into subtasks
    decomp_response = orchestrator_client.chat.completions.create(
        model="gpt-4.1",
        messages=[{
            "role": "user",
            "content": f"Decompose this into ordered GUI subtasks (JSON list): {high_level_goal}"
        }],
        response_format={"type": "json_object"},
    )
    subtasks = json.loads(decomp_response.choices[0].message.content)["subtasks"]

    results = []
    for subtask in subtasks:
        # Step 2: CUA specialist executes each subtask locally
        result = run_computer_use_agent(cua_client, subtask, max_steps=10)
        results.append(result)

        # Step 3: Orchestrator evaluates result and decides whether to continue
        eval_response = orchestrator_client.chat.completions.create(
            model="gpt-4.1",
            messages=[{
                "role": "user",
                "content": f"Subtask: '{subtask}'. Agent result: '{result}'. Did it succeed? Reply YES or NO."
            }],
        )
        if "NO" in eval_response.choices[0].message.content.upper():
            print(f"⚠️ Subtask failed, retrying: {subtask}")
            result = run_computer_use_agent(cua_client, subtask, max_steps=15)
            results.append(result)

    return results
```

This pattern keeps GUI execution on fast, cheap local models (30–40% of tokens) while using cloud models only for high-level reasoning — a cost structure that experienced practitioners report cuts billing by 60–70% versus all-cloud approaches. *(verify this stat before publishing)*

**H2 2026 and beyond** looks like this for local computer use agents:

**Universal Agents Across Every Surface.** Holo3.1's mobile gains (AndroidWorld 79.3%) preview a world where a single model handles web, desktop, and mobile automation without environment-specific fine-tuning. Expect iOS automation support to follow as Apple's accessibility APIs open up to agentic runtimes.

**Sub-Second Step Times.** The current 3.3s step time on DGX Spark with NVFP4 is remarkable but still perceptible. As Blackwell architecture hardware proliferates and FP4 tensor core utilization improves, sub-second per-step latency on consumer hardware is realistic by Q4 2026. That changes the UX equation entirely — agents start to feel like interactive tools rather than background processes.

**On-Device Mobile Agents.** Holo3.1's 0.8B and 4B models hint at the real endpoint: agents running directly on smartphones, with no network dependency. At 4B parameters in Q4 GGUF on an iPhone 17 Pro's 8GB RAM, real-time on-device mobile automation becomes plausible.

**Standardized Action Spaces.** The shift from bespoke JSON schemas to OpenAI-compatible function-calling in Holo3.1 signals an industry standardization drive. Expect a common `computer_use_v1`

tool protocol — analogous to how `openai.tools`

standardized LLM tool use — to emerge from the major players within the next two quarters.

**Privacy-First Enterprise Adoption.** The compliance dam is about to break. As local inference quality reaches parity with cloud models (Holo3.1 is nearly there), enterprise legal and security teams — who have been blocking cloud-based CUA deployments for 18 months — will green-light local deployments. The opportunity for the developer ecosystem is enormous.

The combination of Holo3.1's quantized model family, mature inference runtimes like llama.cpp and vLLM, and the function-calling protocol standardization has removed the last major barrier to **production-grade local computer use agents**.

We've covered a lot of ground here:

The technology is ready. The models are available today on HuggingFace. The inference runtimes are mature and well-documented.

**Your move:** clone the code above, pull `Holo3.1-9B-GGUF`

, and spin up your first local computer use agent this weekend. The era of privacy-preserving, on-device GUI automation has arrived — and it's faster, cheaper, and more capable than the cloud-only approach that preceded it.

*Found this useful? Star the Holo3.1 HuggingFace collection and join the conversation on the HuggingFace Discord. If you build something cool with this, tag me — I'd love to feature it.*

**Tags:** `ai-agents`

`local-llm`

`computer-use`

`quantization`

`llama-cpp`

`holo3`

`generative-ai`

`python`

`langgraph`