Smart Caching Strategies for Cost-Efficient LLM Inference

Welcome back, fellow MLOps enthusiasts! In our previous chapters, we’ve explored the foundations of LLMOps, set up robust inference pipelines, and learned how to dynamically route requests to different models. Now, it’s time to tackle one of the biggest challenges in production LLM systems: managing the high computational cost and latency associated with large language models.

This chapter is all about caching. You’ll discover how implementing smart caching strategies can dramatically reduce your GPU usage, lower inference costs, and significantly improve the responsiveness of your LLM applications. We’ll dive deep into different types of caches, understand why and how they work, and explore their practical applications in real-world scenarios. Get ready to supercharge your LLM deployments!

The LLM Challenge: Why Caching is Critical

Large Language Models are, well, large! They consist of billions of parameters, demanding significant computational resources (especially GPUs) and memory bandwidth for every inference request. Unlike traditional machine learning models that often perform a single forward pass, LLMs generate responses token by token in an auto-regressive manner. This sequential generation process introduces unique challenges:

1. High GPU Memory Footprint: Loading a single large LLM into GPU memory can consume tens or even hundreds of gigabytes.
2. Repetitive Computations: For each subsequent token generated in a sequence, the model needs to attend to all previously generated tokens. This means a lot of redundant computation.
3. Variable Output Lengths: Responses can vary greatly in length, making resource provisioning tricky.
4. Cost: GPU time is expensive. Reducing computation directly translates to cost savings.
5. Latency: Repetitive computations and sequential generation can lead to higher end-to-end latency, impacting user experience.

Caching helps us address these challenges by storing and reusing computation results, avoiding redundant work, and thus saving precious GPU cycles and speeding up responses. Think of it like remembering the answer to a frequently asked question instead of calculating it every single time!

Core Concepts: Types of Caching for LLMs

When we talk about caching in LLMs, we’re not just talking about one type of cache. There are several distinct strategies, each targeting different aspects of the inference process. Let’s break them down.

1. KV Cache (Key-Value Cache)

The KV cache is perhaps the most fundamental and impactful caching mechanism for auto-regressive LLM inference. To understand it, we need a quick refresher on how the self-attention mechanism works in Transformer models (the architecture behind most LLMs).

What it is: In a Transformer’s self-attention layer, the input tokens are transformed into three different vectors for each token: a Query (Q), a Key (K), and a Value (V). To generate the next token, the model calculates attention scores by comparing the current token’s Query vector against the Key vectors of all preceding tokens. These scores are then used to weight the Value vectors of those preceding tokens, summing them up to create a context vector.

Why it’s important: When an LLM generates text token by token, for each new token, it needs to perform attention over all previously generated tokens. Without caching, the Key and Value vectors for the prior tokens would be recomputed every single time. This is incredibly inefficient!

The KV cache stores the Key and Value vectors for all tokens processed so far in the current sequence. When a new token is generated, its Query vector only needs to be compared against the cached Key and Value vectors of the preceding tokens, plus its own newly computed K and V. This avoids recomputing K and V for the entire sequence history, saving significant computation and speeding up generation.

How it functions:

Imagine you’re generating the sentence “The quick brown fox…”.

1. Input: “The”
   • Compute Q, K, V for “The”.
   • Store K, V for “The” in KV cache.
   • Generate next token.
2. Input: “quick” (using “The” as context)
   • Compute Q, K, V for “quick”.
   • Compare “quick”’s Q against “The”’s K (from cache).
   • Combine “quick”’s V with “The”’s V (from cache).
   • Store K, V for “quick” in KV cache (alongside “The”).
   • Generate next token.
3. Input: “brown” (using “The quick” as context)
   • Compute Q, K, V for “brown”.
   • Compare “brown”’s Q against “The”’s K and “quick”’s K (both from cache).
   • Combine “brown”’s V with “The”’s V and “quick”’s V (both from cache).
   • Store K, V for “brown” in KV cache.
   • … and so on.

The KV cache is typically managed internally by specialized LLM inference engines like vLLM (known for PagedAttention), NVIDIA’s TensorRT-LLM, or Hugging Face’s Text Generation Inference (TGI), which are highly optimized for this purpose.

2. Prompt Cache (Prefix Cache)

While the KV cache optimizes within a single generation, the prompt cache works across different requests.

What it is: A prompt cache stores the pre-computed KV cache (or even the full LLM output) for common prompt prefixes. If multiple users or requests start with the exact same initial text, we can reuse the work done for that prefix.

Why it’s important:

• RAG Systems: In Retrieval-Augmented Generation (RAG) systems, the “system prompt” and the retrieved context often form a static, long prefix for many user queries. Caching this prefix means the LLM doesn’t have to re-process the entire context for every single request.
• Chatbots: A chatbot might have a standard “system message” or persona definition that precedes every user query.
• Common Templates: Applications using predefined prompt templates can benefit immensely.

How it functions: When a request comes in, the system checks if its initial segment (the prefix) matches an entry in the prompt cache. If there’s a hit, the system retrieves the cached KV state corresponding to that prefix and then continues generation from there with the remaining, unique part of the prompt. This saves the cost of processing the entire prefix through the LLM.

Example: If your RAG system always starts prompts with: "You are a helpful assistant. Here is some context: [retrieved_context]. Based on this context, answer the user's question: [user_question]"

The "[retrieved_context]" part might be static for a period or across many queries. The prompt cache can store the KV state after processing "[retrieved_context]", so only "[user_question]" needs to be processed by the LLM for subsequent requests using the same context.

3. Semantic Cache

The semantic cache operates at an even higher level, focusing on the meaning of user queries rather than just exact text matches.

What it is: A semantic cache stores the responses from the LLM based on the semantic similarity of the input queries. If a new query is semantically similar enough to a previously answered query, the cached response is returned without even calling the LLM.

Why it’s powerful: Users often ask the same question in slightly different ways. For example, “What’s the weather like today?” and “Current weather conditions?” are semantically very similar. A traditional cache would miss these, but a semantic cache can catch them.

How it functions:

1. Embed the Query: When a user query arrives, it’s first converted into a numerical vector (an embedding) using a smaller, faster embedding model.
2. Similarity Search: This embedding is then used to perform a similarity search in a vector database (e.g., Pinecone, Weaviate, ChromaDB) that stores embeddings of previously answered queries along with their corresponding LLM responses.
3. Threshold Check: If a sufficiently similar query is found (above a defined similarity threshold), the cached LLM response is retrieved and returned.
4. LLM Call & Cache Update: If no sufficiently similar query is found, the request is sent to the LLM. Once the LLM generates a response, the new query’s embedding and its response are stored in the semantic cache for future use.

Trade-offs:

• Freshness: Semantic caches are best for queries where answers don’t change frequently. For real-time information (e.g., stock prices, dynamic data), a semantic cache might return stale data.
• Complexity: Requires an embedding model and a vector database, adding architectural complexity.
• Cost of Embedding: There’s a small cost associated with generating embeddings, but it’s typically far less than a full LLM inference.

Caching Layers in a Production LLM System

These different caching strategies can be combined to form a multi-layered caching architecture, providing maximum efficiency. Let’s visualize how they might fit together.

In this diagram:

• A user request first hits a semantic cache. If a similar query has been answered, the response is returned immediately. This is the fastest and cheapest path.
• If not, the request proceeds to check the prompt cache. If the prompt has a known prefix, the LLM inference starts from a pre-computed KV state.
• Finally, the actual LLM inference occurs, where the underlying inference engine (like vLLM) efficiently manages the KV cache for token-by-token generation.
• New responses might update both the semantic and prompt caches for future requests.

This layered approach ensures you’re leveraging the right cache for the right situation, maximizing efficiency.

Step-by-Step Implementation: Conceptualizing Caching

Implementing a full-fledged KV cache requires deep interaction with LLM inference engines, which handle it internally. However, we can conceptually build out a simple prompt cache and understand how to integrate a semantic cache.

1. Building a Simple Prompt Cache (Python)

Let’s create a basic, in-memory prompt cache using a Python dictionary. This cache will store full LLM responses for specific prompt prefixes.

First, we’ll need a placeholder for our LLM. In a real scenario, this would be an API call or an inference server client.

Create a file named llm_service.py with the following content:

# llm_service.py
import time

def call_llm_api(prompt: str, max_new_tokens: int = 50, temperature: float = 0.7) -> str:
    """
    Simulates an expensive LLM API call.
    In a real application, this would interact with an LLM inference endpoint.
    """
    print(f"--- Calling LLM for prompt: '{prompt[:50]}...' ---")
    time.sleep(2) # Simulate network latency and computation
    # Simulate LLM response based on prompt
    if "weather" in prompt.lower():
        return "The weather is sunny with a slight breeze, 25°C."
    elif "capital of france" in prompt.lower():
        return "The capital of France is Paris."
    elif "python for data science" in prompt.lower():
        return "Python is widely used in data science for its extensive libraries like Pandas, NumPy, and Scikit-learn."
    else:
        return f"This is a simulated LLM response for: '{prompt}'. Max tokens: {max_new_tokens}"

if __name__ == "__main__":
    print(call_llm_api("What is the capital of France?"))
    print(call_llm_api("Tell me about Python for data science."))

Next, create a file named prompt_cache_service.py and add the following code. This will contain our prompt cache logic.

# prompt_cache_service.py
import hashlib
from typing import Dict, Any, Tuple
from llm_service import call_llm_api # Import our simulated LLM

class PromptCache:
    def __init__(self, cache_size: int = 100):
        self.cache: Dict[str, Tuple[str, Dict[str, Any]]] = {} # Stores (response, llm_params)
        self.cache_size = cache_size
        self.lru_keys = [] # For simple LRU eviction

    def _generate_cache_key(self, prompt_prefix: str, llm_params: Dict[str, Any]) -> str:
        """Generates a unique cache key based on prompt prefix and LLM parameters."""
        # It's crucial to include relevant LLM parameters in the cache key
        # because different parameters (e.g., temperature, max_new_tokens)
        # can lead to different responses for the same prompt.
        params_str = ",".join(f"{k}={v}" for k, v in sorted(llm_params.items()))
        return hashlib.sha256(f"{prompt_prefix}-{params_str}".encode('utf-8')).hexdigest()

    def get_or_generate(self, full_prompt: str, prefix_length: int, llm_params: Dict[str, Any]) -> str:
        """
        Attempts to retrieve a response from cache based on a prompt prefix.
        If not found, calls the LLM and caches the result.
        """
        prompt_prefix = full_prompt[:prefix_length]
        cache_key = self._generate_cache_key(prompt_prefix, llm_params)

        if cache_key in self.cache:
            # Move to front for LRU, indicating it was recently used
            self.lru_keys.remove(cache_key)
            self.lru_keys.append(cache_key)
            print(f"[CACHE HIT] for prefix: '{prompt_prefix[:30]}...'")
            return self.cache[cache_key][0] # Return the cached response

        print(f"[CACHE MISS] for prefix: '{prompt_prefix[:30]}...'")
        # If cache miss, call the LLM
        response = call_llm_api(full_prompt, **llm_params)

        # Cache the new response
        if len(self.cache) >= self.cache_size:
            # Evict the least recently used item to make space
            oldest_key = self.lru_keys.pop(0)
            del self.cache[oldest_key]
            print(f"[CACHE EVICTION] Removed oldest item (LRU): '{oldest_key[:10]}...'")

        self.cache[cache_key] = (response, llm_params)
        self.lru_keys.append(cache_key)
        print(f"[CACHE STORED] for prefix: '{prompt_prefix[:30]}...'")
        return response

    def clear(self):
        self.cache.clear()
        self.lru_keys.clear()
        print("[CACHE CLEARED]")

if __name__ == "__main__":
    cache = PromptCache(cache_size=2) # Small cache for demonstration

    # Define common LLM parameters
    default_llm_params = {"max_new_tokens": 50, "temperature": 0.7}

    print("\n--- First set of requests ---")
    response1 = cache.get_or_generate("What is the capital of France?", 20, default_llm_params)
    print(f"Response 1: {response1}")

    response2 = cache.get_or_generate("Tell me about Python for data science.", 20, default_llm_params)
    print(f"Response 2: {response2}")

    print("\n--- Repeating first request (should be a cache hit) ---")
    response3 = cache.get_or_generate("What is the capital of France, give a short answer?", 20, default_llm_params)
    print(f"Response 3: {response3}")

    print("\n--- New request (will cause eviction due to small cache size) ---")
    response4 = cache.get_or_generate("What's the current weather in London?", 20, default_llm_params)
    print(f"Response 4: {response4}")

    print("\n--- Check original cached item (should be a miss now due to eviction) ---")
    response5 = cache.get_or_generate("Tell me about Python for data science in more detail.", 20, default_llm_params)
    print(f"Response 5: {response5}")

    print("\n--- Request with different LLM parameters (should be a miss even if prefix matches) ---")
    response6 = cache.get_or_generate("What is the capital of France?", 20, {"max_new_tokens": 10, "temperature": 0.1})
    print(f"Response 6: {response6}")

    cache.clear()

Explanation:

1. llm_service.py: This file contains a simple call_llm_api function that simulates calling an actual LLM. It includes a time.sleep(2) to mimic the latency of a real API call, making the benefits of caching more apparent.
2. PromptCache Class:
   • __init__: Initializes an empty dictionary self.cache to store responses and self.lru_keys for simple Least Recently Used (LRU) eviction.
   • _generate_cache_key: This is crucial! It creates a unique hash based on the prompt_prefix AND the llm_params. Why include llm_params? Because the same prompt might yield different results if you change temperature, max_new_tokens, or top_p. A robust cache needs to account for these variations.
   • get_or_generate:
     • It extracts a prompt_prefix from the full_prompt based on prefix_length.
     • It generates a cache_key.
     • Cache Hit: If the key exists, it prints [CACHE HIT] and returns the stored response. It also updates lru_keys to mark this item as recently used.
     • Cache Miss: If the key doesn’t exist, it prints [CACHE MISS], calls call_llm_api, and stores the new response along with the llm_params in the cache.
     • Eviction: If the cache cache_size is exceeded, it removes the oldest item (least recently used) before adding the new one.
   • clear: Resets the cache.

Run python prompt_cache_service.py from your terminal and observe the [CACHE HIT] and [CACHE MISS] messages. You’ll see how subsequent requests with the same prefix and parameters are served instantly from the cache, bypassing the simulated 2-second LLM call!

2. Conceptualizing Semantic Cache Integration

Integrating a semantic cache is more involved as it requires an embedding model and a vector database. Here’s a high-level conceptual outline in Python pseudo-code.

Create a file named semantic_cache_service.py and add the following content:

# semantic_cache_service.py
import numpy as np
from typing import Dict, Any, List
from llm_service import call_llm_api # Our simulated LLM

# --- Placeholder for Embedding Model ---
# In reality, this would be a small, fast model (e.g., Sentence-BERT, OpenAI embeddings API)
def get_embedding(text: str) -> List[float]:
    """Simulates getting an embedding for a given text.
    
    WARNING: This is a simplistic dummy implementation for demonstration purposes ONLY.
    DO NOT use this for actual semantic search! Real embedding models are complex
    neural networks that convert text into dense, meaningful vector representations.
    """
    # A real embedding model would convert text into a dense vector
    # For demonstration, we'll use a simple hash-based "embedding"
    hash_val = sum(ord(c) for c in text) % 1000
    # Create a dummy 16-dimensional vector for illustration
    return [float(hash_val) / 1000] * 16

# --- Placeholder for Vector Database Client ---
# In reality, this would be an SDK for Pinecone, Weaviate, ChromaDB, etc.
class VectorDBClient:
    def __init__(self):
        # Stores {'embedding': [...], 'query': '...', 'response': '...'}
        self.data: List[Dict[str, Any]] = [] 

    def upsert(self, embedding: List[float], query: str, response: str):
        """Adds or updates an entry in the simulated vector database."""
        self.data.append({'embedding': embedding, 'query': query, 'response': response})
        print(f"[VECTOR DB] Upserted query: '{query[:30]}...'")

    def search(self, query_embedding: List[float], top_k: int = 1) -> List[Tuple[float, Dict[str, Any]]]:
        """Performs a similarity search in the simulated vector database."""
        if not self.data:
            return []
        
        query_vec = np.array(query_embedding)
        similarities = []
        for item in self.data:
            item_vec = np.array(item['embedding'])
            # Simple cosine similarity (dot product for normalized vectors)
            # For dummy embeddings, this will be very basic.
            dot_product = np.dot(query_vec, item_vec)
            norm_product = np.linalg.norm(query_vec) * np.linalg.norm(item_vec)
            similarity = dot_product / (norm_product + 1e-9) if norm_product != 0 else 0 # Avoid division by zero
            similarities.append((similarity, item))
        
        similarities.sort(key=lambda x: x[0], reverse=True)
        return similarities[:top_k]

# --- Semantic Cache Service ---
class SemanticCache:
    def __init__(self, vector_db: VectorDBClient, similarity_threshold: float = 0.9):
        self.vector_db = vector_db
        self.similarity_threshold = similarity_threshold

    def get_or_generate(self, query: str, llm_params: Dict[str, Any]) -> str:
        """
        Attempts to retrieve a response from semantic cache.
        If not found, calls the LLM and caches the result.
        """
        query_embedding = get_embedding(query)
        
        # 1. Search semantic cache
        search_results = self.vector_db.search(query_embedding, top_k=1)

        if search_results:
            best_match_sim, best_match_item = search_results[0] 
            if best_match_sim >= self.similarity_threshold:
                print(f"[SEMANTIC CACHE HIT] for query: '{query[:30]}...' (Similarity: {best_match_sim:.2f})")
                return best_match_item['response']
        
        print(f"[SEMANTIC CACHE MISS] for query: '{query[:30]}...'")
        # 2. If miss, call LLM
        response = call_llm_api(query, **llm_params)
        
        # 3. Cache the new result
        self.vector_db.upsert(query_embedding, query, response)
        print(f"[SEMANTIC CACHE STORED] for query: '{query[:30]}...'")
        return response

if __name__ == "__main__":
    vector_db = VectorDBClient()
    # Using a high similarity threshold for the dummy embeddings for clearer demonstration.
    # In a real scenario, this threshold would be tuned based on your embedding model and data.
    semantic_cache = SemanticCache(vector_db, similarity_threshold=0.95) 

    default_llm_params = {"max_new_tokens": 50, "temperature": 0.7}

    print("\n--- First semantic query ---")
    response1 = semantic_cache.get_or_generate("What is the weather like today?", default_llm_params)
    print(f"Response 1: {response1}")

    print("\n--- Similar semantic query (should be a hit with high threshold if dummy embeddings align) ---")
    response2 = semantic_cache.get_or_generate("Could you tell me the current weather conditions?", default_llm_params)
    print(f"Response 2: {response2}")

    print("\n--- Different semantic query (should be a miss) ---")
    response3 = semantic_cache.get_or_generate("What's the capital of Germany?", default_llm_params)
    print(f"Response 3: {response3}")

    print("\n--- Slightly different query, might be a miss with dummy embeddings, or a hit if lucky ---")
    response4 = semantic_cache.get_or_generate("Tell me about today's forecast.", default_llm_params)
    print(f"Response 4: {response4}")

Explanation:

1. get_embedding: This is a placeholder for an actual embedding model. In a production system, you’d use a robust model (e.g., sentence-transformers library, OpenAIEmbeddings, CohereEmbeddings). Our dummy get_embedding is just for structural demonstration. It produces a very basic vector based on a hash, so semantic similarity will be rudimentary.
2. VectorDBClient: This simulates a client interacting with a vector database. It stores embeddings, original queries, and LLM responses. Its search method performs a basic similarity calculation (cosine similarity) to find the most relevant cached entry.
3. SemanticCache Class:
   • __init__: Takes a VectorDBClient instance and a similarity_threshold.
   • get_or_generate:
     • It first gets an embedding for the incoming query.
     • It then searches the vector_db for similar embeddings.
     • Cache Hit: If a match is found above the similarity_threshold, it returns the cached response.
     • Cache Miss: If no sufficiently similar match is found, it calls the call_llm_api.
     • Cache Update: After getting a new response, it upserts (inserts or updates) the query’s embedding, query, and response into the vector_db.

Running python semantic_cache_service.py with its dummy embeddings might not always produce perfect semantic hits for slightly varied queries due to the simplistic get_embedding function. However, it clearly demonstrates the workflow and the architectural components required for a semantic cache. With a real embedding model and vector database, the hit rate for semantically similar queries would be much higher!

Mini-Challenge: Enhancing the Prompt Cache

The current PromptCache works well, but what if you want to cache responses that have the same prefix but might have different max_new_tokens? Currently, if max_new_tokens differs, it’s a cache miss even for the same prefix.

Challenge: Modify the PromptCache class to allow caching of responses for the same prompt prefix but with different max_new_tokens values. The idea is that if you have a cached response for a prefix, and a new request asks for fewer max_new_tokens than what’s cached, you should be able to truncate the cached response and return it as a hit.

Hint:

• You’ll need to store the max_new_tokens used to generate the cached response when you first store it. This can be part of the value stored in self.cache.
• When a new request comes in, if there’s a prefix match, check if the cached max_new_tokens is greater than or equal to the requested max_new_tokens.
• If so, you can truncate the cached response. For simplicity, you can just take the first N words from the cached response, where N is proportional to the requested max_new_tokens.
• Remember to still account for other llm_params (like temperature) in your cache key, as they fundamentally change the generation process for the start of the response.

What to observe/learn: This challenge highlights the complexities of cache key design and how to handle variations in request parameters while still maximizing cache hits. It forces you to think about what constitutes a “reusable” cached item and the trade-offs involved in truncating content.

Common Pitfalls & Troubleshooting

1. Stale Cache Data:
   • Pitfall: Returning outdated information, especially critical for semantic caches or prompt caches where the underlying data or model might have changed.
   • Troubleshooting: Implement robust cache invalidation strategies. This could be:
     • Time-to-Live (TTL): Automatically expire cache entries after a certain period.
     • Event-Driven Invalidation: Invalidate relevant cache entries when the source data changes (e.g., a new document is added to your RAG knowledge base, or a new model version is deployed).
     • Manual Invalidation: Provide an API endpoint to explicitly clear parts of or the entire cache.
2. Over-caching vs. Under-caching:
   • Pitfall:
     • Over-caching: Caching too many unique items that are rarely re-requested can lead to high memory consumption without significant hit rates, increasing infrastructure costs.
     • Under-caching: Missing opportunities to cache frequently requested items, leading to unnecessary LLM calls.
   • Troubleshooting: Monitor cache hit rates and memory usage. Adjust cache sizes (e.g., cache_size in our PromptCache) based on observed patterns. Analyze query logs to identify common prefixes or semantically similar queries that are good candidates for caching.
3. Incorrect Cache Key Design:
   • Pitfall: Not including all relevant parameters in the cache key (e.g., temperature, max_new_tokens, model_version, user_ID for personalized responses), leading to incorrect cached responses being returned.
   • Troubleshooting: Rigorously review your cache key generation logic. Ensure every parameter that can influence the LLM’s output is part of the key. Test edge cases with varying parameters.
4. KV Cache Memory Bloat:
   • Pitfall: The KV cache can consume a lot of GPU memory, especially with long sequences and large batch sizes. This can lead to Out-Of-Memory (OOM) errors.
   • Troubleshooting:
     • Use specialized inference engines (vLLM, TensorRT-LLM, TGI) that implement advanced KV cache management techniques like PagedAttention (vLLM) for efficient memory sharing.
     • Quantize your models to reduce memory footprint.
     • Monitor GPU memory usage diligently using tools like nvidia-smi or cloud provider monitoring dashboards.

Summary

Congratulations! You’ve navigated the intricate world of LLM caching. Here are the key takeaways from this chapter:

• LLM Inference is Costly: Large models, sequential generation, and repetitive computations make LLM inference resource-intensive and expensive.
• KV Cache is Fundamental: It optimizes token-by-token generation by storing attention Key and Value vectors, avoiding redundant computations for past tokens. It’s managed by advanced inference engines like vLLM.
• Prompt Cache Saves Prefix Processing: By storing pre-computed KV states or full responses for common prompt prefixes, it reduces redundant LLM calls for templated or context-heavy prompts.
• Semantic Cache Handles Variations: It uses embeddings and vector databases to return cached responses for semantically similar queries, even if the exact wording differs, significantly cutting LLM calls.
• Layered Caching for Maximum Efficiency: Combining these strategies creates a powerful, cost-efficient inference pipeline.
• Careful Design is Key: Cache key design, invalidation strategies, and monitoring are crucial for effective caching.

Caching is an indispensable tool in your MLOps arsenal for building scalable, performant, and cost-effective LLM applications. By intelligently storing and reusing computation, you can deliver a snappier user experience while keeping your cloud bills in check.

Next, we’ll shift our focus to even deeper optimizations, exploring advanced GPU usage and fine-tuning specialized runtimes to squeeze every drop of performance out of your LLM infrastructure!

References

• Microsoft Learn: LLMOps workflows on Azure Databricks
• GitHub: NVIDIA TensorRT-LLM
• GitHub: vLLM - A high-throughput and memory-efficient LLM serving engine
• Hugging Face: Text Generation Inference (TGI)
• Pinecone: What is a vector database?
• Weaviate: What is a Vector Database?

This page is AI-assisted and reviewed. It references official documentation and recognized resources where relevant.