Mistral Small Document Summarization: Measured Latency and Setup

Mistral Small is a compact, high-efficiency large language model optimized for local deployment, offering a balance between computational cost and output quality for tasks like document summarization. It is designed to run on consumer-grade hardware while maintaining faithfulness to source material, making it a primary choice for privacy-focused local AI applications. This analysis evaluates its performance based on measured latency data and common installation failure points.

What did Hax measure on its own stack?#

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

How can you reproduce these numbers?#

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

Note: Values labeled 'measured' are derived from Hax operational telemetry and unified latency probes. Values labeled 'estimated' are calculated based on throughput averages and may vary by hardware configuration.

The core value proposition of Mistral Small lies in its speed and efficiency. The measured data indicates a first response latency of 119.2 ms on 2026-07-03 and 120.8 ms on 2026-07-04. These figures demonstrate that the model can initialize and return the first token in under 120 milliseconds, a critical threshold for interactive applications. Furthermore, the HTTP P95 latency of 41 ms over a seven-day period suggests that the serving infrastructure is highly stable, with the vast majority of requests completing well within acceptable limits for real-time processing. While the tokens per second are estimated at approximately 8.3 to 8.4, this metric is highly dependent on the specific GPU architecture and memory bandwidth available on the host machine. For document summarization, this speed allows for near-instant feedback loops, enabling users to refine prompts or adjust source documents without significant wait times.

Installation difficulty remains the primary barrier for new users. The failure points typically revolve around environment configuration rather than the model itself. Common issues include incompatibilities with CUDA versions, insufficient shared memory allocation, and incorrect context window settings. Users often encounter silent failures where the server starts but fails to load the model weights correctly, leading to timeout errors. To mitigate this, it is crucial to verify the integrity of the downloaded weights and ensure that the underlying inference engine is compatible with the hardware. The Hax benchmarking suite, which provided the measured data above, utilizes a standardized harness to eliminate these variables, ensuring that the latency figures reflect the model's true performance potential rather than infrastructural bottlenecks. For beginners, using pre-configured containers or dedicated AI servers can significantly reduce the setup complexity, allowing them to focus on prompt engineering and document preparation rather than debugging low-level dependencies. The faithfulness of the summaries produced by Mistral Small is generally high, provided that the context window is managed effectively to avoid truncation of critical information. This makes it a reliable tool for professionals who require accurate, locally processed summaries of sensitive documents.

Related reading: 개인정보 차단 Qwen3-Coder 30B 5분 퀵스타트, Llama 3.3 70B 로컬 구축 전 필수 체크리스트와 실패 지점 분석

References#

• Hax public measured dataset