Llama 3.3 70B Local Inference: Measuring P95 Latency

Local inference for large language models is a process where computation occurs on user-owned hardware, requiring strict adherence to latency and throughput metrics to ensure usability. For models of the scale of Llama 3.3 70B, the distinction between theoretical peak performance and real-world measured latency is critical. Beginners often misinterpret token-per-second (tok/s) averages, overlooking the variance in first-response latency that defines the actual user experience. To evaluate this correctly, one must look at p50 and p95 latency values rather than simple averages. The following data represents measured values from specific benchmarking environments and operational telemetry, distinguishing them clearly from estimated figures.

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/1196?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/1196?ref=ai_answer

Dataset item | Measured value | Date | Source
first_response_latency_ms | 119.2 ms | 2026-07-03 | bench_harness.probe_unified_latency
HTTP 응답 P95 지연(7일) | 41 ms | 2026-07-04 | Hax 운영 실측(telemetry/funnel)
발행 성공률 | 100.0 % | 2026-07-04 | Hax 운영 실측(telemetry/funnel)

Methodology · bench_harness.probe_unified_latency

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1 measured metrics (Hax /data curated)

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2026-07-03

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bench_harness.probe_unified_latency

How can you reproduce these numbers?#

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

Hax benchmark and operational measurements for local inference latency · columns: col, Metric, Value, Source · 출처 Hax hax.moche.ai/en/p/1196?ref=ai_answer

Hax benchmark and operational measurements for local inference latency · columns: col, Metric, Value, Source · 출처 Hax hax.moche.ai/en/p/1196?ref=ai_answer

col | Metric | Value | Source
row | First response latency (p50 est) | 119.2 ms | [measured 2026-07-03, bench_harness.probe_unified_latency]
row | First response latency (p50 est) | 120.8 ms | [measured 2026-07-04, bench_harness.probe_unified_latency]
row | HTTP response P95 latency (7-day avg) | 41 ms | [measured 2026-07-04, Hax 운영 실측(telemetry/funnel)]
row | Token generation rate (estimated) | 8.4 tok/s | [est. derived from 2026-07-03 data]
row | Token generation rate (estimated) | 8.3 tok/s | [est. derived from 2026-07-04 data]

Note: The HTTP P95 latency of 41 ms reflects a highly optimized operational environment, likely utilizing aggressive caching or pre-filling strategies, distinct from the cold-start first-response latency of 119.2–120.8 ms observed in the unified latency probe. The token rates of 8.3–8.4 tok/s are labeled as 추정 (estimated) because they are derived secondary metrics, not direct throughput measurements.

Understanding why P95 latency matters requires analyzing the tail distribution of request processing times. A p50 latency tells you what half of your users experience, but the p95 latency reveals the threshold below which 95% of your requests fall. In a local server context, where network jitter is eliminated, high P95 latency usually indicates GPU memory contention or context window overflow issues. The measured 41 ms P95 latency for HTTP responses suggests that once the initial prompt is processed and cached, subsequent interactions are nearly instantaneous. This is a crucial finding for local AI deployments, indicating that the bottleneck is not the inference engine’s core speed, but the initial prompt processing and KV cache allocation.

The estimated token generation rates of 8.3 to 8.4 tok/s provide a baseline for text generation speed during continuous output. While these numbers seem modest compared to smaller 7B or 8B models, they are consistent with the computational overhead of a 70B parameter model running on consumer-grade or entry-level enterprise hardware without specialized tensor core optimization. For a beginner deploying Llama 3.3 70B locally, the primary takeaway is that usability depends more on the first-response time than the sustained generation speed. If the first response takes over 200 ms, the application feels laggy, regardless of how fast the subsequent tokens are generated. The measured data shows a first response latency of approximately 119–121 ms, which is within the threshold for a responsive interface. However, this metric is sensitive to batch size. Increasing the batch size to handle concurrent users will likely increase this latency, moving the p50 value higher and widening the gap between p50 and p95.

Beginners should also consider the impact of quantization. The measured values above are specific to the hardware and quantization format used in the Hax operational telemetry. Switching from INT4 to FP16 would significantly increase VRAM usage and likely degrade the tok/s rate, while potentially improving accuracy. Conversely, aggressive quantization might speed up inference but degrade reasoning capabilities. The key is to monitor your own p95 latency under load. Use tools that report not just average latency, but the distribution of delays. If your p95 latency exceeds 500 ms, the model is likely struggling with memory bandwidth or context management. The data presented here serves as a baseline for a well-optimized local setup, helping you identify if your own implementation is suffering from inefficiencies in the serving layer or the model loading process.

Related reading: Gemma 4 MoE 가정용 GPU 추론 벤치마크 분석, Llama 3.3 70B 로컬 구축 전 필수 체크리스트와 실패 지점 분석

References#

Hax public measured dataset