Mistral Small: Evaluating Local AI Response Speed with Latency Metrics

Mistral Small is a lightweight, high-performance large language model optimized for local deployment, enabling users to run advanced natural language processing tasks on personal hardware while maintaining strict data privacy. For beginners seeking a step-by-step guide to document summarization, the core challenge lies not just in accuracy, but in perceiving responsiveness through technical metrics like p50 and p95 latency. Understanding these metrics is essential for judging whether a local AI setup feels instantaneous or sluggish during real-world usage.

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/1209?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/1209?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

표본

1 measured metrics (Hax /data curated)

수집일

2026-07-03

방법

bench_harness.probe_unified_latency

How can you reproduce these numbers?#

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

The distinction between average latency and percentile latency is critical. P50 latency represents the median response time, meaning half of all requests are faster than this value. P95 latency, however, captures the experience of the slowest 5% of requests, which is often where user frustration begins. In local AI deployment, hardware bottlenecks such as GPU memory bandwidth or CPU cache limits can cause significant variance between these two values.

Hax Performance Benchmarks & Estimates · columns: Metric, Measured Value (Hax), Estimated Context · 출처 Hax hax.moche.ai/en/p/1209?ref=ai_answer

Metric | Measured Value (Hax) | Estimated Context
First Response Latency | 119.2 ms [measured 2026-07-03] | Baseline cold start
First Response Latency | 120.8 ms [measured 2026-07-04] | Warm cache scenario
HTTP Response P95 Latency | 41 ms [measured 2026-07-04] | 7-day operational average
Token Generation Rate | 8.4 tok/s [estimated] | Derived from latency
Faithfulness Score | 92% [estimated] | Document summary accuracy

Note: Measured values are derived from Hax internal telemetry and bench_harness.probe_unified_latency tools. Estimated values are contextual approximations based on standard hardware configurations.

When setting up Mistral Small locally, the first response latency is the primary metric for perceived speed. The measured data from Hax shows a first response latency of 119.2 ms on 2026-07-03 and 120.8 ms on 2026-07-04. These values indicate a highly optimized inference engine where the initial token generation occurs within a fraction of a second, a crucial threshold for maintaining user engagement. The token generation rate, estimated at 8.4 tokens per second, further supports this efficiency, allowing for rapid document summarization without noticeable pauses.

For document summarization tasks, faithfulness is paramount. While speed matters, the model must accurately reflect the source material. Mistral Small’s architecture is designed to balance this trade-off, offering high fidelity in summaries even under constrained local resources. The HTTP response P95 latency of 41 ms, measured over a 7-day period in Hax’s operational environment, demonstrates exceptional stability. This low tail latency ensures that even under load, the system remains responsive, providing a consistent user experience.

Beginners should focus on monitoring these latencies during their initial setup. Use tools that can report p50 and p95 values to gain a holistic view of performance. If the p95 latency spikes significantly above the p50, it may indicate hardware contention or inefficient memory management. By grounding their evaluation in these measurable facts, users can make informed decisions about their local AI infrastructure, ensuring both speed and reliability in their document processing workflows.