The Efficiency Moat - Navigating the New Economics of AI Inference

In the initial “gold rush” of Generative AI, competitive advantage was measured in parameters. Organizations scrambled to deploy the largest models possible, treating inference—the process of serving a model to users—as a secondary operational cost.

However, as AI moves into the “Inference Era,” the strategic focus has shifted. The bottleneck is no longer the model’s intelligence, but the efficiency of its delivery and inference. Today’s leaders are realizing that Inference Efficiency is the only sustainable moat. This briefing explores the cutting-edge optimizations in engines like vLLM and SGLang, providing a roadmap for turning raw compute into scalable business value.

The Memory Constraint - Rethinking the KV Cache

Every Large Language Model (LLM) possesses a “short-term memory” known as the KV (Key-Value) Cache. In legacy systems, this memory was managed statically, leading to massive waste.

1. PagedAttention (vLLM)

• What’s the Innovation here: Borrowing from operating system architecture, PagedAttention partitions the KV cache into flexible “pages.”
• Business Impact: Reduces memory fragmentation from ~60% to under 4%. This allows for 4x higher throughput on the same hardware.
• Strategic Use Case: High-volume batch processing (e.g., summarizing millions of support tickets) where maximizing density per GPU is critical.

2. RadixAttention (SGLang)

• What’s the Innovation here: Treating the KV cache as a persistent Radix Tree. If a new request shares a prefix (like a standardized legal prompt or a large context document), SGLang reuses that memory instantly.
• Business Impact: Near-zero Time to First Token (TTFT).
• Strategic Use Case: Multi-turn conversational agents and Retrieval-Augmented Generation (RAG). Every subsequent turn in a chat becomes “free” in terms of prefill compute.

Breaking the Prefill Bottleneck

The “Prefill” stage (when the model reads your prompt) is compute-intensive and often stalls the “Decode” stage (when the model types the answer). Orchestrating this transition is vital for user experience.

1. Chunked Prefill:

Breaks massive prompts into smaller segments. This prevents a user sending a 50-page PDF from “freezing” the response for twenty other users on the same server.

• Switch: --enable-chunked-prefill (vLLM).

2. Multi-Step Scheduling:

Available in vLLM V1, this allows the engine to plan steps of generation ahead. By reducing the frequency of CPU-to-GPU handshakes, it eliminates the “latency floor” for high-performance chips.

3. Disaggregated Serving (The Mooncake Architecture):

The ultimate evolution where you separate physical hardware into “Prefill Nodes” and “Decode Nodes.” Strategic Use Case: Enterprise-scale APIs where you must maintain a strict Service Level Agreement (SLA) for latency regardless of request length.

Decoding & Kernel Optimizations

These switches control how the model generates tokens and how the underlying CUDA kernels are executed.

1. Speculative Decoding

• The Concept: A small “draft” model (e.g., TinyLlama) quickly predicts the next 3–5 tokens. The large “target” model (e.g., Llama-3 70B) verifies them in a single parallel step. If the draft is right, you get 5 tokens for the cost of 1 target model pass.
• vLLM Switch: —speculative-model [draft_model_name] and —num-speculative-tokens [N].
• SGLang Switch: —speculative-algo [EAGLE, LOOKAHEAD] and —speculative-draft [model_path].

Performance Impact: Reduces end-to-end latency by 1.5x to 2.5x, though it increases total GPU compute load.

2. Continuous Batching

• The Concept: Instead of waiting for an entire batch to finish (static), tokens are added to the batch the moment a previous request finishes.
• Switch: In both engines, this is the core engine behavior, but controlled via —max-num-seqs or —max-num-batched-tokens.

Performance Impact: Maximizes GPU utilization by keeping the “bus” full at all times.

Distributed Architecture: SPMD, MPMD, and GSPMD

Scaling beyond a single GPU requires a paradigm for distribution.

1. SPMD (Single Program, Multiple Data):

How does it works: Every GPU executes the exact same code on its own data slice. This is the backbone of Tensor Parallelism. You run the exact same program (the model architecture) on every GPU. However, each GPU works on a different piece of the data.

How it fits:

• Tensor Parallelism (TP): Each GPU has a “slice” of the model’s weight matrices. When an input comes in, all GPUs execute the same layer simultaneously on their respective slices and then “sync” (all-reduce) to get the final result.

• Data Parallelism (DP): Multiple replicas of the full model (or TP group) run on different GPUs. Each replica processes a completely different batch of user requests.

Performance Impact: It is the most efficient for high-bandwidth interconnects (like NVLink). Since every GPU is doing the same thing at the same time, the overhead of “deciding what to do next” is zero.

2. MPMD (Multiple Program, Multiple Data):

How does it works: MPMD is less common for simple inference but is the architecture behind Disaggregated Serving and Pipeline Parallelism. Different GPUs run different parts of the code. One set of GPUs might run the “Prefill” stage (processing the prompt), while another set runs the “Decode” stage (generating tokens).

How it fits:

• Pipeline Parallelism (PP): GPU 1 runs layers 1–20, GPU 2 runs layers 21–40, and so on. They are technically running different “programs” (different chunks of the model).

• Disaggregated Prefill/Decode: This is a hot topic in 2024/2025. You might have a high-compute cluster (H100s) running the prefill “program” and a high-memory cluster (A100s) running the decode “program.”

Performance Impact: It helps overcome memory capacity limits and can improve hardware utilization by matching the specific stage of inference to the best-suited hardware.

3. GSPMD (General SPMD):

GSPMD is a more advanced, compiler-driven version of SPMD, primarily used in Google’s XLA compiler and JAX, and is now being integrated into vLLM’s TPU backend.A compiler (like XLA on Google TPUs) automatically shards the model. The developer writes code for a “virtual” giant GPU, and the software handles the reality of the 100-chip cluster.

How does it works: In traditional SPMD, the developer must manually write communication code (e.g., dist.all_reduce). In GSPMD, the developer writes code as if it’s for a single huge GPU. They just add “sharding hints” (annotations), and the compiler automatically figures out how to split the tensors and insert the communication calls.

How it fits:

• vLLM on TPU: vLLM uses GSPMD to enable model parallelism on TPU slices. It allows vLLM to scale models like Llama-3 70B across hundreds of TPU cores without the developers having to rewrite the core CUDA kernels for every possible hardware configuration.

• Automatic Parallelism: It allows the engine to switch between different sharding strategies (e.g., switching from Tensor Parallel to Fully Sharded Data Parallel) just by changing a config, without changing the model code.

Use Case: Rapid scaling on TPU v6/v7 slices where manual orchestration would take months.

Here is the information converted into a clean Markdown table for your technical documentation.

Parallel Programming Models in Inference Engines

Deep Expert Parallelism (EP): The MoE Breakthrough

The rise of Mixture-of-Experts (MoE) models like DeepSeek-V3 has introduced a new challenge: Expert Parallelism. In an MoE model, each token only “talks” to a few specialized subnetworks (experts).

How Deep EP Works

Traditionally, routing tokens to experts on different GPUs created a “traffic jam” (the All-to-All bottleneck). Deep EP (Expert Parallelism) solves this through:

• Asymmetric Communication: Using specialized kernels (like the DeepEP library) that overlap the communication of token data with the actual computation of the neural network.
• The Formula: If experts are sharded across GPUs, the router determines which expert gets token . Deep EP ensures that the data for is streamed to the correct GPU exactly as the previous calculation ends.
• Load Balancing (EPLB): SGLang’s Expert Parallelism Load Balancer prevents “Expert Hotspots.” If one expert (e.g., “the coder”) is being overworked by a specific batch, the system can dynamically replicate that expert or shift workloads.

The Performance Impact

• VRAM Efficiency: You can serve a 671B parameter model (like DeepSeek-V3) across just a few nodes because each GPU only needs to store its assigned experts.
• Throughput Scaling: By overlapping communication, the model achieves near-linear scaling. You get the intelligence of a massive model with the speed of a much smaller one.

The Decision Matrix

The Path Forward

The transition to these advanced inference stacks is not just a technical upgrade; it is a financial one. As compute costs continue to be the primary line item for AI ventures, the ability to tune these switches—specifically Deep Expert Parallelism for MoE models—will define the winners of the next decade.