Mistral AI Performance Tuning

Mistral AI Performance Tuning

Overview

Optimize Mistral AI API performance with caching, batching, and latency reduction techniques.

Prerequisites

• Mistral AI SDK installed
• Understanding of async patterns
• Redis or in-memory cache available (optional)
• Performance monitoring in place

Latency Benchmarks

Instructions

Step 1: Response Caching

import { LRUCache } from 'lru-cache';
import crypto from 'crypto';

const cache = new LRUCache<string, any>({
  max: 1000,
  ttl: 5 * 60 * 1000, // 5 minutes
  updateAgeOnGet: true,
});

function getCacheKey(messages: any[], model: string, options?: any): string {
  const data = JSON.stringify({ messages, model, options });
  return crypto.createHash('sha256').update(data).digest('hex');
}

async function cachedChat(
  client: Mistral,
  messages: any[],
  model: string,
  options?: { temperature?: number; maxTokens?: number }
): Promise<string> {
  // Only cache deterministic requests (temperature = 0)
  const isCacheable = (options?.temperature ?? 0.7) === 0;

  if (isCacheable) {
    const key = getCacheKey(messages, model, options);
    const cached = cache.get(key);
    if (cached) {
      console.log('Cache hit');
      return cached;
    }
  }

  const response = await client.chat.complete({
    model,
    messages,
    ...options,
  });

  const content = response.choices?.[0]?.message?.content ?? '';

  if (isCacheable) {
    const key = getCacheKey(messages, model, options);
    cache.set(key, content);
  }

  return content;
}

Step 2: Redis Distributed Caching

import Redis from 'ioredis';
import crypto from 'crypto';

const redis = new Redis(process.env.REDIS_URL);

async function cachedWithRedis<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttlSeconds = 300
): Promise<T> {
  const cached = await redis.get(key);
  if (cached) {
    return JSON.parse(cached);
  }

  const result = await fetcher();
  await redis.setex(key, ttlSeconds, JSON.stringify(result));
  return result;
}

// Semantic cache for similar queries
async function semanticCache(
  client: Mistral,
  query: string,
  threshold = 0.95
): Promise<string | null> {
  // Get embedding for query
  const queryEmbed = await client.embeddings.create({
    model: 'mistral-embed',
    inputs: [query],
  });
  const queryVector = queryEmbed.data[0].embedding;

  // Check cache for similar queries
  const cachedQueries = await redis.keys('semantic:*');

  for (const key of cachedQueries) {
    const cached = JSON.parse(await redis.get(key) || '{}');
    const similarity = cosineSimilarity(queryVector, cached.embedding);

    if (similarity >= threshold) {
      console.log(`Semantic cache hit (similarity: ${similarity.toFixed(3)})`);
      return cached.response;
    }
  }

  return null;
}

Step 3: Request Batching

import DataLoader from 'dataloader';

// Batch embedding requests
const embeddingLoader = new DataLoader<string, number[]>(
  async (texts) => {
    const response = await client.embeddings.create({
      model: 'mistral-embed',
      inputs: texts as string[],
    });
    return response.data.map(d => d.embedding);
  },
  {
    maxBatchSize: 100, // Mistral limit
    batchScheduleFn: callback => setTimeout(callback, 10), // 10ms window
  }
);

// Usage - automatically batched
const [embed1, embed2, embed3] = await Promise.all([
  embeddingLoader.load('Text 1'),
  embeddingLoader.load('Text 2'),
  embeddingLoader.load('Text 3'),
]);

Step 4: Connection Optimization

import { Agent } from 'https';
import Mistral from '@mistralai/​mistralai';

// Keep-alive connection pooling
const agent = new Agent({
  keepAlive: true,
  maxSockets: 10,
  maxFreeSockets: 5,
  timeout: 60000,
});

// Note: Check if Mistral client supports custom agents
// If not, connection pooling happens at the HTTP level

Step 5: Streaming for Perceived Performance

// Streaming reduces Time to First Token (TTFT)
async function* streamWithMetrics(
  client: Mistral,
  messages: any[],
  model: string
): AsyncGenerator<{ content: string; metrics: any }> {
  const startTime = Date.now();
  let firstTokenTime: number | null = null;
  let tokenCount = 0;

  const stream = await client.chat.stream({ model, messages });

  for await (const event of stream) {
    const content = event.data?.choices?.[0]?.delta?.content;
    if (content) {
      if (!firstTokenTime) {
        firstTokenTime = Date.now();
      }
      tokenCount++;
      yield {
        content,
        metrics: {
          ttft: firstTokenTime - startTime,
          tokensPerSecond: tokenCount / ((Date.now() - startTime) / 1000),
        },
      };
    }
  }
}

// Usage
let fullResponse = '';
for await (const { content, metrics } of streamWithMetrics(client, messages, 'mistral-small-latest')) {
  fullResponse += content;
  process.stdout.write(content);
}
console.log(`\nTTFT: ${metrics.ttft}ms, Speed: ${metrics.tokensPerSecond.toFixed(1)} tok/​s`);

Step 6: Model Selection for Speed

type SpeedTier = 'fastest' | 'balanced' | 'quality';

function selectModelForSpeed(tier: SpeedTier, taskComplexity: 'low' | 'medium' | 'high'): string {
  const matrix = {
    fastest: {
      low: 'mistral-small-latest',
      medium: 'mistral-small-latest',
      high: 'mistral-small-latest',
    },
    balanced: {
      low: 'mistral-small-latest',
      medium: 'mistral-small-latest',
      high: 'mistral-large-latest',
    },
    quality: {
      low: 'mistral-small-latest',
      medium: 'mistral-large-latest',
      high: 'mistral-large-latest',
    },
  };

  return matrix[tier][taskComplexity];
}

Step 7: Performance Monitoring

interface PerformanceMetrics {
  model: string;
  latencyMs: number;
  ttftMs?: number;
  tokensPerSecond?: number;
  inputTokens: number;
  outputTokens: number;
  cached: boolean;
}

async function measurePerformance(
  operation: () => Promise<any>,
  metadata: Partial<PerformanceMetrics>
): Promise<{ result: any; metrics: PerformanceMetrics }> {
  const start = Date.now();

  const result = await operation();

  const metrics: PerformanceMetrics = {
    model: metadata.model || 'unknown',
    latencyMs: Date.now() - start,
    inputTokens: result.usage?.promptTokens || 0,
    outputTokens: result.usage?.completionTokens || 0,
    cached: metadata.cached || false,
    ...metadata,
  };

  // Log to monitoring system
  console.log('[PERF]', JSON.stringify(metrics));

  return { result, metrics };
}

// Usage
const { result, metrics } = await measurePerformance(
  () => client.chat.complete({ model, messages }),
  { model, cached: false }
);

Output

• Reduced API latency
• Caching layer implemented
• Request batching enabled
• Performance monitoring active

Error Handling

Examples

Quick Performance Wrapper

const withPerformance = async <T>(
  name: string,
  fn: () => Promise<T>
): Promise<T> => {
  const start = Date.now();
  const result = await fn();
  console.log(`[${name}] ${Date.now() - start}ms`);
  return result;
};

// Usage
const response = await withPerformance('chat', () =>
  client.chat.complete({ model, messages })
);

Parallel Requests with Concurrency Limit

import pLimit from 'p-limit';

const limit = pLimit(5); // Max 5 concurrent requests

const results = await Promise.all(
  prompts.map(prompt =>
    limit(() => client.chat.complete({
      model: 'mistral-small-latest',
      messages: [{ role: 'user', content: prompt }],
    }))
  )
);

Resources

• Mistral AI Models
• LRU Cache Documentation
• DataLoader Documentation

Next Steps

For cost optimization, see mistral-cost-tuning.