Llama 3.3 70B Local Inference: A 5-Minute Quick Start Guide

Llama 3.3 70B is a quantized large language model optimized for local server inference, allowing users to run high-performance AI without cloud dependencies. It represents the current standard for balancing reasoning capability and hardware accessibility for private deployments. The primary goal of this guide is to establish a functional inference environment in five minutes, focusing on immediate operational feedback rather than perfect configuration. For beginners, the most critical factor is hardware selection; you need a consumer-grade GPU with at least 24GB of VRAM, such as the NVIDIA RTX 3090 or 4090, to run the 4-bit quantized version effectively. Lower-end cards will fail to load the model into memory, resulting in immediate out-of-memory (OOM) errors.

What did Hax measure on its own stack?#

Reference numbers Hax measured directly on its own infrastructure (measured, sourced).

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1202?ref=ai_answer

Hax /data matched measured block (measured, 2026-07-03) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1202?ref=ai_answer

Dataset item | Measured value | Date | Source
first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency
qwen-image(50스텝, 1024px, 콜드) 생성 시간 | 73 s | 2026-06-30 | Hax ComfyUI 풀 실측
z-image-turbo(8스텝, 1024px, 콜드) 생성 시간 | 6 s | 2026-06-30 | Hax ComfyUI 풀 실측

측정 방법론 · bench_harness.probe_unified_latency +1 more

표본

3 measured metrics (Hax /data curated)

측정 환경

RTX PRO 6000 Blackwell ×4 풀; ComfyUI 0.24.0

수집일

2026-06-30 ~ 2026-07-03

방법

bench_harness.probe_unified_latency; 1장 콜드 스타트(모델 로드 포함); 1장 콜드 스타트

How can you reproduce these numbers?#

Follow the source column above and our open dataset at /data.

Hax Environment Assessment - Estimated Metrics · columns: gpu_model, vram_required, time_to_first_token, status · 출처 Hax hax.moche.ai/en/p/1202?ref=ai_answer

gpu_model | vram_required | time_to_first_token | status
RTX 3090 | 24GB | 1.2s | estimated
RTX 4090 | 24GB | 0.8s | estimated
Hax Server | Not Specified | Not Measured | 측정대기

Note: All throughput and latency metrics in this table are estimates based on standard 4-bit quantization benchmarks. Actual performance varies significantly based on system cooling, background processes, and specific prompt lengths. Hax has not measured these specific values in our production environment as of this publication.

The installation process begins with selecting an inference engine. Ollama is the most beginner-friendly option due to its streamlined command-line interface. It automatically handles model downloading, quantization, and weight loading. To start, install Ollama via the official installer for your operating system. Once installed, open a terminal and execute the command 'ollama run llama3.3'. The system will verify the model hash and download approximately 47GB of data. This download phase is the first major failure point for users with slow or unstable internet connections. Ensure you have sufficient disk space before initiating the download.

After the model is loaded, you can test inference by typing a prompt. Monitor the terminal output for tokens per second (t/s). A healthy system should achieve at least 15-20 t/s for the 4-bit model on an RTX 4090. If the rate drops below 5 t/s, check for thermal throttling or CPU bottlenecks. Common failure points include incorrect GPU driver versions and insufficient swap space on Linux systems. For more advanced users, llama.cpp offers greater control over batching and quantization but requires manual compilation and configuration. Batching allows the server to process multiple requests simultaneously, improving throughput but increasing VRAM usage. Start with a batch size of 512 and adjust based on memory availability. Do not attempt to run the full 16-bit model on consumer hardware; it requires over 140GB of VRAM. Stick to Q4_K_M quantization for the best balance of speed and quality. Always verify your hardware compatibility before proceeding, as mismatched expectations are the primary cause of abandoned setups.