Llama 3.3 70B Local Inference: Setup, Throughput & Quality Analysis

Llama 3.3 70B is a dense large language model optimized for high-accuracy instruction following, designed to deliver performance comparable to larger parameter counts while reducing computational overhead. This optimization allows for efficient local server deployment, enabling users to run sophisticated AI workloads on consumer-grade hardware without relying on cloud APIs. For beginners looking to validate quality degradation through numerical metrics, setting up a local inference environment is the first critical step. The process involves downloading the GGUF quantized weights, selecting an inference engine like llama.cpp or Ollama, and configuring the server to handle batched requests.

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/1200?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/1200?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 Evaluation Environment: Llama 3.3 70B Q4_K_M on RTX 4090 · columns: col, Metric, Value · 출처 Hax hax.moche.ai/en/p/1200?ref=ai_answer

col | Metric | Value
Row 1 | Hardware | NVIDIA RTX 4090 24GB
Row 2 | Throughput (Tokens/s) | Not Measured (측정대기)
Row 3 | Latency (First Token) | Not Measured (측정대기)
Row 4 | Accuracy vs. 70B Base | Estimated (추정) Higher

Note: All performance metrics are estimates based on community benchmarks; Hax has not conducted independent measurement for this specific configuration yet.

To begin the setup, ensure your server has sufficient VRAM. The 70B model in Q4_K_M quantization requires approximately 38-40 GB of memory, which often necessitates CPU offloading or multi-GPU setups if a single 24GB card is insufficient. Using Ollama simplifies this by handling GPU/CPU memory splitting automatically. Once the model is pulled, you can start the inference server. The next step is evaluating quality. Quality degradation in local models often manifests as reduced coherence in long-context windows or factual hallucinations in niche domains.

To quantify this, users should run a standardized benchmark such as MMLU or a custom truthfulness test. For example, query the model with a complex logical puzzle or a detailed historical fact. Compare the output against a known ground truth. If the model produces plausible but incorrect information, this is a measurable error. Throughput batching allows the server to process multiple requests simultaneously, improving token per second (tok/s) rates. However, high batching can increase latency for individual users. Balancing these factors requires monitoring CPU and GPU utilization. Real-world validation involves running the model for sustained periods to check for memory leaks or consistency drops. By analyzing error rates and throughput numbers side-by-side, developers can determine if the quantized version meets their specific accuracy requirements. This data-driven approach ensures that the trade-off between speed and quality is explicitly understood and acceptable for the intended application.