vLLM's Wide Expert Parallelism Makes DeepSeek Inference 10x More Efficient at Scale

The open-source inference engine vLLM has achieved a significant milestone: serving DeepSeek's massive mixture-of-experts model at 2,200 tokens per second per H200 GPU. The secret is a technique called "wide expert parallelism" (wide-ep), and it represents a potential inflection point for anyone trying to run frontier-scale models without burning through their compute budget.

The vLLM team published these benchmarks in a technical blog post detailing their approach to large-scale serving. For context, DeepSeek's models are among the most demanding to serve efficiently—they use a mixture-of-experts (MoE) architecture with hundreds of billions of parameters distributed across specialized sub-networks. Running them at production scale has historically required either massive GPU clusters or accepting painful latency tradeoffs.

Why Wide Expert Parallelism Changes the Math

Traditional approaches to serving MoE models face a fundamental bottleneck: expert routing. When a token arrives, the model must decide which subset of experts to activate, then distribute the computation across those experts. This routing step creates communication overhead that kills throughput as you scale to more GPUs.

Wide expert parallelism takes a different approach. Instead of sharding experts across GPUs in the conventional way, wide-ep distributes the expert computation more broadly, reducing the all-to-all communication that typically strangles performance. The result: near-linear scaling as you add more H200 GPUs to the serving cluster.

The 2,200 tokens per second figure is particularly notable because it's measured per GPU. This isn't aggregate throughput across a massive cluster—it's the efficiency you get from each H200 in your deployment. That distinction matters enormously for cost calculations.

What This Means for Inference Economics

Let's do some rough math. An H200 GPU costs roughly $30,000-40,000 (when you can get one). At 2,200 tokens per second, you're looking at approximately 190 million tokens per day from a single GPU. For a typical conversational AI application generating 500-token responses, that's around 380,000 responses daily.

Compare this to the token pricing from major API providers. At current rates, 190 million tokens of output from a frontier model through an API would cost thousands of dollars per day. Self-hosting with vLLM's optimizations starts looking economically viable at much lower volumes than previously assumed.

This doesn't mean everyone should immediately spin up their own DeepSeek deployment. The operational complexity of running inference at scale remains substantial. But for companies already running significant AI workloads, the breakeven point just shifted dramatically in favor of self-hosting.

The Technical Foundation: vLLM's Architecture

vLLM, developed initially at UC Berkeley and now maintained by a growing open-source community, has become the default choice for high-performance LLM inference. Its core innovation—PagedAttention—treats the key-value cache like virtual memory, enabling efficient batching of requests with varying sequence lengths.

The wide-ep extension builds on this foundation. By rethinking how expert parallelism interacts with vLLM's memory management and scheduling systems, the team has eliminated several bottlenecks that previously made MoE models particularly expensive to serve.

Key technical decisions in the implementation include:

• Overlapping expert computation with communication to hide latency
• Optimized all-to-all collective operations using NVIDIA's NCCL library
• Dynamic load balancing across experts to prevent stragglers
• Memory-efficient routing that minimizes fragmentation

The H200's increased memory bandwidth (4.8 TB/s compared to H100's 3.35 TB/s) provides additional headroom, but the architectural optimizations would benefit any recent NVIDIA datacenter GPU.

The Broader Trend: Open Models Getting Easier to Deploy

This benchmark arrives at a pivotal moment for open-source AI infrastructure. DeepSeek, the Chinese AI lab, has released some of the most capable openly-available models, but their size has made deployment impractical for many organizations. vLLM's optimizations lower that barrier substantially.

We're seeing a pattern: as frontier model architectures become public (whether through open releases or through distillation and replication), the competitive moat shifts from "can you build a good model" to "can you serve it efficiently." Companies like Together AI, Fireworks AI, and Groq have built businesses on inference optimization. vLLM's open-source improvements put pressure on the entire inference-as-a-service market.

For enterprises evaluating AI strategy, the message is clear: the cost of running capable AI models in-house is dropping faster than most forecasts predicted. What required a specialized team and custom infrastructure two years ago is becoming a standard deployment pattern.

What's Next

The vLLM team's roadmap suggests further optimizations are coming. Speculative decoding, better prefix caching, and improved support for multimodal models are all in development. The wide-ep work specifically opens the door to efficiently serving even larger MoE models—relevant as labs continue pushing model scale.

For builders working with DeepSeek or similar MoE architectures, the immediate takeaway is practical: if you've been held back by inference costs, it's time to re-run your calculations. The economics may have just shifted in your favor.

For the industry more broadly, efficient open-source inference infrastructure accelerates the commoditization of AI capabilities. When running a frontier-class model becomes a solved problem, differentiation moves up the stack—to applications, data, and integration depth. That's the transition vLLM is quietly enabling, one optimization at a time.