Local Llama 3.3 70B Inference: Quick Start Guide

Llama 3.3 70B is a 70-billion parameter large language model optimized for high-quality reasoning, structured output, and efficient local inference. It enables businesses to process sensitive data on-premise without sending it to public cloud APIs, directly addressing privacy concerns while reducing long-term operational costs. This guide outlines the immediate steps to deploy the model on a local server for routine office automation tasks.

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/1204?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/1204?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.

The primary barrier to entry is hardware compatibility. The 70B parameter count requires significant memory bandwidth. To run the full precision model, you need a GPU with at least 80GB of VRAM, such as an NVIDIA A100. However, for most small-to-medium business setups, 4-bit quantization is the standard approach. This reduces the memory footprint to approximately 38-40GB, allowing the model to run on two NVIDIA RTX 4090s (24GB each) or a single A100 (40GB+ variant). This configuration is the baseline for the '5-minute quickstart' concept, assuming a pre-configured environment using containerized solutions like Ollama or llama.cpp.

Hax Performance Baseline 2024 · columns: Hardware, Throughput (tokens/s), Status · 출처 Hax hax.moche.ai/en/p/1204?ref=ai_answer

Hardware | Throughput (tokens/s) | Status
Dual RTX 4090 (4-bit) | 12-15 tokens/s | estimated
Single A100 80GB (4-bit) | 20-25 tokens/s | estimated
Hax Internal Server | 0 tokens/s | not measured

Deployment begins with installing a lightweight inference engine. Ollama is recommended for beginners due to its command-line simplicity. After downloading the binary, the command 'ollama run llama3.3' initiates the download and launch of the model. This process handles the weight loading automatically. For higher throughput requirements, such as batch processing of invoices or legal documents, llama.cpp with Metal or CUDA acceleration provides more granular control over layer offloading. The key metric for 'time saving' is not just generation speed but also context window utilization. Llama 3.3 supports up to 128k context, allowing for the ingestion of large documents in a single prompt.

For repetitive office tasks, the model excels at structured data extraction. By prompting it to output JSON, you can automate the parsing of emails, contracts, or meeting notes. The 'batching' strategy involves splitting large datasets into manageable chunks that fit within the context window while maintaining coherence. A naive approach sends one document at a time, which is slow. A smarter approach groups related documents if the total token count remains under the 128k limit. This reduces the overhead of model initialization per request.

Note: Throughput values depend heavily on prompt length, temperature settings, and specific hardware drivers. Real-world performance may vary. Always test with your actual data volume before full deployment. The estimated throughput figures above are based on community benchmarks for 4-bit quantized models and are not measured by Hax internal servers at this time. Use these numbers for capacity planning only. Local inference provides full data sovereignty, which is the primary value proposition over API-based services for sensitive corporate information.

Related reading: Llama 3.3 70B 로컬 구축 전 필수 체크리스트와 실패 지점 분석, 가정용 GPU로 Gemma 4 MoE 일상 업무 자동화 실측 분석

References#

Hax public measured dataset