feat(inference-engineering): reuse AI Engineer roadmap resources

Add 39 resources already curated on the AI Engineer roadmap to 22
Inference Engineering topics where they fit (model selection and
evaluation, latency/cost trade-offs, caching, long context, embeddings,
ASR, VLMs, diffusion, desktop inference). At most 4 new links per
topic; existing links are kept and everything stays type-ordered.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

roadmaps/inference-engineering/content/auto-speech-recognition@6PRICRK6aRQCoThxnevB0.md

@@ -5,4 +5,6 @@ ASR models like Whisper take audio input and produce text transcripts. Whisper i
 Visit the following resources to learn more:
 
 - [@official@OpenAI Whisper Models on Hugging Face](https://huggingface.co/openai)
+- [@official@OpenAI Whisper](https://openai.com/index/whisper/)
+- [@opensource@openai/whisper](https://github.com/openai/whisper)
 - [@article@Robust Speech Recognition via Large-Scale Weak Supervision (Whisper)](https://arxiv.org/abs/2212.04356)

roadmaps/inference-engineering/content/bert-style-models@ef3EneMibOaDQkKcpipGY.md

@@ -4,5 +4,7 @@ BERT-style encoder-only models are the traditional architecture for text embeddi
 
 Visit the following resources to learn more:
 
+- [@official@SentenceTransformers Documentation](https://sbert.net/)
+- [@official@Using Sentence Transformers at Hugging Face](https://huggingface.co/docs/hub/sentence-transformers)
 - [@article@BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)
 - [@article@Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)

roadmaps/inference-engineering/content/caching-approaches@2nGv7_8PTWYr7uqbudCcX.md

@@ -5,4 +5,6 @@ KV cache reuse is the primary caching strategy in LLM inference, covering both p
 Visit the following resources to learn more:
 
 - [@opensource@LMCache](https://github.com/LMCache/LMCache)
-- [@article@CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion](https://arxiv.org/abs/2405.16444)
+- [@article@CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion](https://arxiv.org/abs/2405.16444)
+- [@article@What is Prompt Caching?](https://www.ibm.com/think/topics/prompt-caching)
+- [@video@What is Prompt Caching? Optimize LLM Latency with AI Transformers](https://www.youtube.com/watch?v=u57EnkQaUTY)

roadmaps/inference-engineering/content/client-integration@3RkQpvRGWh1Hp_qHlTiXX.md

@@ -1,3 +1,7 @@
 # Client Integration
  
-The client layer connects application code to the inference service and contributes its own slice of end-to-end latency. This group covers the connection overhead, async request patterns, and protocol choices that determine how efficiently client and server communicate.
+The client layer connects application code to the inference service and contributes its own slice of end-to-end latency. This group covers the connection overhead, async request patterns, and protocol choices that determine how efficiently client and server communicate.
+
+Visit the following resources to learn more:
+
+- [@article@OpenAI-Compatible API](https://bentoml.com/llm/llm-inference-basics/openai-compatible-api)

roadmaps/inference-engineering/content/cost-estimation@Gof4kW2LbkNCtH68c7Xvq.md

@@ -1,3 +1,7 @@
 # Cost Estimation
  
-Dedicated inference cost depends on batch sizing, traffic patterns, and sequence lengths rather than simply on token count. Comparing dedicated versus API costs requires converting per-token API pricing into total cost over a representative time window and comparing it to GPU-hour costs at the same traffic volume. Total cost of ownership also includes engineering time for building and maintaining the inference system.
+Dedicated inference cost depends on batch sizing, traffic patterns, and sequence lengths rather than simply on token count. Comparing dedicated versus API costs requires converting per-token API pricing into total cost over a representative time window and comparing it to GPU-hour costs at the same traffic volume. Total cost of ownership also includes engineering time for building and maintaining the inference system.
+
+Visit the following resources to learn more:
+
+- [@article@The LLM Inference Trilemma: Throughput, Latency, Cost](https://www.digitalocean.com/blog/llm-inference-tradeoffs)

roadmaps/inference-engineering/content/desktop-inference@fQDl7_3nRGa8NFivOIgTP.md

@@ -4,4 +4,6 @@ Desktop inference runs on consumer hardware, primarily Apple M-series unified me
 
 Visit the following resources to learn more:
 
+- [@official@Ollama](https://ollama.com/)
+- [@official@LM Studio](https://lmstudio.ai/)
 - [@opensource@ComfyUI](https://github.com/comfyanonymous/ComfyUI)

roadmaps/inference-engineering/content/diffusion-models@aagMwPPmi6ngfkIqZL-Mg.md

@@ -7,4 +7,6 @@ Visit the following resources to learn more:
 - [@article@Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
 - [@article@High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)
 - [@article@SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://arxiv.org/abs/2307.01952)
-- [@article@Scalable Diffusion Models with Transformers (DiT)](https://arxiv.org/abs/2212.09748)
+- [@article@Scalable Diffusion Models with Transformers (DiT)](https://arxiv.org/abs/2212.09748)
+- [@article@How DALL-E 2 Actually Works](https://www.assemblyai.com/blog/how-dall-e-2-actually-works/)
+- [@video@How AI Image Generators Work (Stable Diffusion / DALL-E)](https://www.youtube.com/watch?v=1CIpzeNxIhU)

roadmaps/inference-engineering/content/embedding-models@Tvt-vX1gWuJZJBHh5vfKu.md

@@ -4,7 +4,9 @@ Embedding models convert variable-length text or image input into a fixed-length
 
 Visit the following resources to learn more:
 
+- [@official@Hugging Face Embedding Models](https://huggingface.co/models?pipeline_tag=feature-extraction)
 - [@article@BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)
 - [@article@Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
 - [@article@Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147)
-- [@article@MTEB: Massive Text Embedding Benchmark](https://arxiv.org/abs/2210.07316)
+- [@article@MTEB: Massive Text Embedding Benchmark](https://arxiv.org/abs/2210.07316)
+- [@article@What Are Embedding Models? Benefits and Best Practices](https://cohere.com/blog/embedding-models)

roadmaps/inference-engineering/content/fine-tuning@fWJplrAuUStEMNNLQc75G.md

@@ -4,4 +4,6 @@ Fine-tuning takes a pretrained foundation model and adapts it to a specific doma
 
 Visit the following resources to learn more:
 
-- [@article@Adversarial Diffusion Distillation](https://arxiv.org/abs/2311.17042)
+- [@article@Adversarial Diffusion Distillation](https://arxiv.org/abs/2311.17042)
+- [@article@What is Fine-Tuning?](https://www.ibm.com/think/topics/fine-tuning)
+- [@article@How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model](https://towardsdatascience.com/how-i-fine-tuned-granite-vision-2b-to-beat-a-90b-model-insights-and-lessons-learned/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)

roadmaps/inference-engineering/content/inference-vs-training@fVUfQYb8BP_hFMmd1S2-J.md

@@ -4,4 +4,6 @@ Training is the process of learning model weights from data. Inference is servin
 
 Visit the following resources to learn more:
 
-- [@article@Language Models Are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
+- [@article@Language Models Are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
+- [@article@Inference vs Training](https://www.cloudflare.com/learning/ai/inference-vs-training/)
+- [@article@What is Machine Learning Inference?](https://hazelcast.com/glossary/machine-learning-inference/)

roadmaps/inference-engineering/content/large-language-models@irgGvEnY1_WW1EPtuMKgC.md

@@ -4,4 +4,7 @@ An LLM is a decoder-only transformer with three main components: an embedding la
 
 Visit the following resources to learn more:
 
+- [@article@What is a Large Language Model (LLM)?](https://www.cloudflare.com/en-gb/learning/ai/what-is-large-language-model/)
+- [@article@New to LLMs? Start Here](https://towardsdatascience.com/new-to-llms-start-here/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
+- [@video@How Large Language Models Work](https://www.youtube.com/watch?v=5sLYAQS9sWQ)
 - [@book@Build a Large Language Model From Scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)

roadmaps/inference-engineering/content/latency-vs-throughput@kvNIi3aY5rn69_hYkDyUK.md

@@ -1,3 +1,7 @@
 # Latency vs. Throughput
  
-Lower latency means faster responses per user; higher throughput means more total requests served per unit time. These goals are often in tension. Increasing batch size improves throughput but worsens per-user latency. Choosing the right balance is one of the primary decisions in configuring an inference service.
+Lower latency means faster responses per user; higher throughput means more total requests served per unit time. These goals are often in tension. Increasing batch size improves throughput but worsens per-user latency. Choosing the right balance is one of the primary decisions in configuring an inference service.
+
+Visit the following resources to learn more:
+
+- [@article@The LLM Inference Trilemma: Throughput, Latency, Cost](https://www.digitalocean.com/blog/llm-inference-tradeoffs)

roadmaps/inference-engineering/content/long-context-handling@uL3Fn8XfS1Yv_PJd9R8wi.md

@@ -5,4 +5,7 @@ Long sequences produce KV caches large enough to strain GPU memory, slow down de
 Visit the following resources to learn more:
 
 - [@article@Ring Attention with Blockwise Transformers for Near-Infinite Context](https://arxiv.org/abs/2310.01889)
-- [@article@RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864)
+- [@article@RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864)
+- [@article@What is Long Context and Why Does it Matter for AI?](https://cloud.google.com/transform/the-prompt-what-are-long-context-windows-and-why-do-they-matter)
+- [@article@Long-Context LLM Infrastructure: Building Systems for Million-Token Windows](https://introl.com/blog/long-context-llm-infrastructure-million-token-windows-guide)
+- [@video@Deep Dive into Long Context](https://www.youtube.com/watch?v=NHMJ9mqKeMQ)

roadmaps/inference-engineering/content/model-evaluation@lVf5GoJF_PVTkTiQFTNVg.md

@@ -5,4 +5,7 @@ Model evaluation, or evals, is the practice of systematically measuring model qu
 Visit the following resources to learn more:
 
 - [@article@How to Fine-Tune Qwen3 to GPT-4o Level Performance](https://ghost.oxen.ai/how-to-fine-tune-qwen3-to-gpt-4o-level-performance/)
+- [@article@LLM Evaluation: A Beginner's Guide](https://www.evidentlyai.com/llm-guide/llm-evaluation)
+- [@article@LLM Evaluation Metrics and Methods, Explained Simply](https://www.evidentlyai.com/llm-guide/llm-evaluation-metrics)
+- [@article@A Pragmatic Guide to LLM Evals for Devs](https://newsletter.pragmaticengineer.com/p/evals)
 - [@book@Evals for AI Engineers](https://www.oreilly.com/library/view/evals-for-ai/9798341660717/)

roadmaps/inference-engineering/content/model-selection@jQAWZ1plw4_wPE1UvE_Ux.md

@@ -4,4 +4,6 @@ The most important decision in model performance optimization is which model to
 
 Visit the following resources to learn more:
 
+- [@article@Choosing the Right Model](https://bentoml.com/llm/getting-started/choosing-the-right-model)
+- [@article@Beyond Vibes: How to Properly Select the Right LLM for the Right Task](https://aws.amazon.com/blogs/machine-learning/beyond-vibes-how-to-properly-select-the-right-llm-for-the-right-task/)
 - [@book@AI Engineering: Building Applications with Foundation Models](https://www.oreilly.com/library/view/ai-engineering/9781098166298/)

roadmaps/inference-engineering/content/open-vs-closed-models@KAt5FzglOUtH7jLlzDn-I.md

@@ -4,4 +4,6 @@ Closed models are accessed exclusively through a vendor API; the weights are nev
 
 Visit the following resources to learn more:
 
+- [@article@Open-Source vs. Closed LLMs: A Guide](https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/)
+- [@video@Open Source vs Closed AI: LLMs, Agents & the AI Stack Explained](https://www.youtube.com/watch?v=_QfxGZGITGw)
 - [@book@AI Engineering: Building Applications with Foundation Models](https://www.oreilly.com/library/view/ai-engineering/9781098166298/)

roadmaps/inference-engineering/content/reasoning-models@Y9YkcfblXHEvS-gdEWwaE.md

@@ -1,3 +1,7 @@
 # Reasoning Models
  
-Reasoning models generate an intermediate thinking sequence before producing their final output, adding a third token sequence alongside the input and output. This reasoning trace increases total token count significantly and changes inference economics: TTFT is higher, total latency is longer, and output token costs dominate. Inference engineers must account for the variable and sometimes very long reasoning sequences when sizing infrastructure and setting latency budgets.
+Reasoning models generate an intermediate thinking sequence before producing their final output, adding a third token sequence alongside the input and output. This reasoning trace increases total token count significantly and changes inference economics: TTFT is higher, total latency is longer, and output token costs dominate. Inference engineers must account for the variable and sometimes very long reasoning sequences when sizing infrastructure and setting latency budgets.
+
+Visit the following resources to learn more:
+
+- [@article@Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903)

roadmaps/inference-engineering/content/shared-vs-dedicated-inf@2DsjfW9r4dOI3LDQU80qt.md

@@ -1,3 +1,8 @@
 # Shared vs. Dedicated Inference
  
-Shared inference means sending requests to a public API and paying per token. Dedicated inference means renting GPUs and running your own model server, paying per GPU-hour. Shared inference is simpler and has no cold start, but dedicated inference offers control over latency, cost at scale, model customization, and uptime. Most products start shared and move to dedicated as traffic grows.
+Shared inference means sending requests to a public API and paying per token. Dedicated inference means renting GPUs and running your own model server, paying per GPU-hour. Shared inference is simpler and has no cold start, but dedicated inference offers control over latency, cost at scale, model customization, and uptime. Most products start shared and move to dedicated as traffic grows.
+
+Visit the following resources to learn more:
+
+- [@article@Serverless vs. Self-Hosted LLM Inference](https://bentoml.com/llm/llm-inference-basics/serverless-vs-self-hosted-llm-inference)
+- [@article@Self-Hosted LLM: A Practical Guide for DevOps](https://www.plural.sh/blog/self-hosting-large-language-models/)

roadmaps/inference-engineering/content/streaming--protocol-support@SGQCvuHAkg4TcJa_e2f-y.md

@@ -1,3 +1,7 @@
 # Streaming and Protocol Support
  
-Text streaming over HTTP is sufficient for LLM chat applications. For real-time voice and video applications, WebSockets provide a persistent bidirectional connection for continuous unstructured data like audio chunks. gRPC provides bidirectional streaming for structured service-to-service communication with schema enforcement. Each WebSocket connection holds a slot on the server, and the concurrency limit must be set to match the GPU's sustainable load.
+Text streaming over HTTP is sufficient for LLM chat applications. For real-time voice and video applications, WebSockets provide a persistent bidirectional connection for continuous unstructured data like audio chunks. gRPC provides bidirectional streaming for structured service-to-service communication with schema enforcement. Each WebSocket connection holds a slot on the server, and the concurrency limit must be set to match the GPU's sustainable load.
+
+Visit the following resources to learn more:
+
+- [@article@AI for Web Devs: Faster Responses with HTTP Streaming](https://austingil.com/ai-for-web-devs-streaming/)

roadmaps/inference-engineering/content/transformers--diffusers@H_e3KZ7p7enUCYE5M5Lr7.md

@@ -5,4 +5,5 @@ The transformers and diffusers libraries provide reference implementations of po
 Visit the following resources to learn more:
 
 - [@official@Hugging Face Diffusers](https://huggingface.co/docs/diffusers/index)
-- [@official@Hugging Face Transformers](https://huggingface.co/docs/transformers/index)
+- [@official@Hugging Face Transformers](https://huggingface.co/docs/transformers/index)
+- [@course@Hugging Face Course](https://www.youtube.com/playlist?list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o)

roadmaps/inference-engineering/content/video-processing-for-vlms@7pRZu7xa2_43HVwKty9px.md

@@ -1,3 +1,7 @@
 # Video Processing for VLMs
  
-One second of video contains 24 frames, each requiring roughly 1,000 tokens to represent at full resolution. This makes video inputs extremely long-context workloads. Frame rate reduction and downsampling are practically mandatory for running video through a VLM in a single request. Prefix caching and chunked prefill become critical for managing the resulting multi-thousand-token sequences.
+One second of video contains 24 frames, each requiring roughly 1,000 tokens to represent at full resolution. This makes video inputs extremely long-context workloads. Frame rate reduction and downsampling are practically mandatory for running video through a VLM in a single request. Prefix caching and chunked prefill become critical for managing the resulting multi-thousand-token sequences.
+
+Visit the following resources to learn more:
+
+- [@opensource@Awesome LLMs for Video Understanding](https://github.com/yunlong10/Awesome-LLMs-for-Video-Understanding)

roadmaps/inference-engineering/content/vision-language-models@JsjmQrN1V_dM1Kw6UnYLc.md

@@ -7,4 +7,5 @@ Visit the following resources to learn more:
 - [@article@BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597)
 - [@article@Visual Instruction Tuning](https://arxiv.org/abs/2304.08485)
 - [@article@Segment Anything](https://arxiv.org/abs/2304.02643)
-- [@article@SpecVLM: Fast Speculative Decoding in Vision-Language Models](https://arxiv.org/abs/2509.11815)
+- [@article@SpecVLM: Fast Speculative Decoding in Vision-Language Models](https://arxiv.org/abs/2509.11815)
+- [@course@A Multimodal World - Hugging Face Computer Vision Course](https://huggingface.co/learn/computer-vision-course/en/unit4/multimodal-models/a_multimodal_world)