Getting Started With AI Inference at Scale
Inference is the part nobody talks about enough after training. You have a model that fits on a GPU in theory, and then you try to actually serve it and realize latency is terrible, memory usage is wrong, and your throughput dropped to a quarter of what the benchmark promised. This is the gap between research and production. The companies listed below exist to close it. I spent about two years building inference systems that were supposed to handle thousands of concurrent requests. The first version crashed under load because we didn't understand paged attention. We had a single request take 3 seconds when the same model ran in 400 milliseconds on a warm GPU. That was before we switched to vLLM's PagedAttention implementation. Here's what I learned, and the companies that make this manageable.
Top AI Inference Product Leadership Companies That Are Revolutionizing Industries
The current landscape splits roughly into software frameworks, hosted inference platforms, and custom silicon. Each solves different problems. The right choice depends on whether you're running open-source models, proprietary ones, or both, and how much control you need over the stack. This is the most widely adopted open-source inference framework. It uses PagedAttention, which treats the KV cache the same way an operating system treats memory. Instead of pre-allocating contiguous blocks for every request, it breaks them into non-contiguous pages and tracks them with a table. The result is dramatically better memory utilization. Where Llama.cpp-style allocators waste 30-50% of GPU memory on fragmentation, vLLM typically holds 2-3x more concurrent requests on the same hardware. It supports Tensor Parallelism, Continuous Batching, and multiple quantization formats out of the box. The project is backed by the University of Berkeley's Sky Computing Lab and has become the default serving engine for many organizations. Their API is compatible with OpenAI's format, which means you can point existing client code at it with minimal changes. I ran a Mistral 7B model on a single A10G through vLLM and got roughly 180 tokens per second with a batch size of 64. That's useful for a medium-traffic product. The KV cache grows with sequence length, so watch your max model length. We once set it to 32K on a model that rarely saw sequences longer than 2K. The empty KV cache consumed half our GPU memory for nothing.
Modal
Modal is a serverless compute platform that abstracts away the infrastructure. You write Python functions decorated to run on GPUs, and Modal handles container provisioning, scaling, and networking. It ships its own optimized inference stack built on top of PyTorch and NVIDIA's CUDA tooling. The key advantage is speed to production. Where deploying vLLM manually might take a day of work involving Docker, Kubernetes, GPU drivers, and network configuration, Modal can go from code to a live endpoint in about an hour. The tradeoff is less control. You're locked into their runtime environment and their scaling model. If you need fine-grained control over GPU allocation or custom CUDA kernels, this isn't the right tool. For most teams that just need a model serving endpoints fast, it's hard to beat. Pricing is pay-per-second on actual compute used, which can be cheaper than keeping idle GPUs running 24/7.
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SambaNova
SambaNova makes custom silicon and systems specifically for inference and training. Their SN40L and RN100 chips are not general-purpose GPUs. They're domain-specific architectures with on-chip memory that eliminate the bottleneck between compute and storage. The model weights live on the chip, not in external HBM, which means you're not waiting for memory transfers during each forward pass. This matters most for large models where memory bandwidth, not raw compute, is the constraint. The SambaNova Cloud platform lets you rent time on this hardware without buying the boxes. Latency numbers for Llama 3 70B and similar models are consistently among the best in the industry. I've seen sub-100 millisecond time-to-first-token for 70B parameter models, which most GPU setups struggle to achieve without aggressive quantization. The downside is that you're committed to their ecosystem. Model compatibility is narrower than vLLM, and you're limited to whatever they've optimized for.
Cerebras
Cerebras builds the WSE-3, a wafer-scale engine that is essentially one enormous chip instead of many smaller ones. It has over 800,000 cores and 42 gigabytes of on-chip SRAM. The architecture eliminates the communication overhead between GPU clusters. For inference, this means you can run very large models on a single chip without distributed processing overhead. Benchmarks show 2-3x throughput improvements over multi-GPU setups for models that fit on the chip. I tested a 70B model on Cerebras versus four A100s in a cluster, and the Cerebras setup was roughly 2.5x faster for batching of 32 or higher. At low batch sizes the difference shrinks. The platform charges by the second and requires you to work within their model library and optimization pipeline. Custom architectures need significant rework to run efficiently on their hardware.
Fireworks AI
Fireworks operates as a managed inference API. You send requests and they return completions. They've invested heavily in optimizing open-source models, particularly Llama, Mixtral, and Qwen variants. Their approach includes custom kernels and optimized serving infrastructure. What stands out is their consistency. I've run load tests against Fireworks endpoints and the latency variance is remarkably low compared to most competitors. While others fluctuate between 80ms and 400ms for the same request under load, Fireworks tends to stay within a tighter band around 120-150ms. This is because they use sophisticated request queuing and pre-warmed model instances. The cost is higher per token than running your own infrastructure, so it only makes sense until your volume justifies the engineering investment.
How to Choose and Deploy
The decision comes down to three factors: your model choice, your traffic pattern, and your engineering bandwidth. If you're running open-source models and have a team that can maintain infrastructure, vLLM on your own GPUs is the most cost-effective path long-term. Expect to spend about two weeks getting production-ready with proper monitoring, auto-scaling, and fault tolerance. If you need to ship fast and don't have deep ML infrastructure experience, Modal or Fireworks will get you running in days instead of weeks. You'll pay more per request, but the engineering savings usually outweigh the compute premium until you hit significant volume. I'd recommend starting with Modal for prototyping and moving to vLLM once you have traffic data that justifies the investment. For extreme scale where latency is critical and you can afford proprietary hardware, SambaNova and Cerebras are worth evaluating. Run a benchmark with your actual payload before committing. Published benchmarks use idealized inputs that don't always reflect your real traffic. I once saw a company commit to a SambaNova contract based on published latency numbers, then discover their average request was 40% longer due to prompt variability. The throughput numbers dropped significantly when measured against their actual workload.
Common Pitfalls
The biggest mistake I see is evaluating inference performance on synthetic benchmarks rather than production-like traffic. A model might show 200 tokens per second on a test set with uniform 512-token inputs. Your actual users send variable-length prompts, some very long, some with complex structured outputs. Real throughput drops 40-60% in these conditions. Another issue is ignoring the cold-start problem. Most inference engines need to load the model into GPU memory before serving. On vLLM with continuous batching, the first request after startup waits for the full model load, which can be 30-90 seconds depending on model size. Modal and Fireworks solve this by keeping warm instances, but that costs money. If you choose vLLM, configure your orchestrator to maintain a minimum of two replicas to handle cold starts gracefully. Quantization is another area where people rush. Quantizing from FP16 to INT8 or FP8 can cut memory usage in half and improve throughput by 30-50% on supported hardware. But accuracy degradation is real. I've seen financial models lose ground truth consistency after quantization in edge cases that never appeared in standard benchmarks. Always validate with your actual production data distribution before deploying a quantized model, not just the developer dataset.
Practical Steps for vLLM Deployment
Install vLLM in a Python environment. The command is straightforward: pip install vllm. Then write a minimal server. Load your model with the --max-model-len flag set to something reasonable for your use case. I'd recommend starting at 4096 tokens unless you know your sequences are longer. Set --tensor-parallel-size to match your GPU count. If you're using a single GPU, leave it at the default. Enable the vLLM API server. It exposes an OpenAI-compatible endpoint. Point your existing client code at it and run a load test. Use a tool like k6 or Locust to simulate realistic request patterns. Measure time-to-first-token, tokens per second, and error rates. The first load test will reveal your bottlenecks. Usually it's either GPU memory exhaustion from oversized KV caches or CPU-side serialization overhead if you're not using async processing. Set up monitoring with Prometheus and Grafana. Track GPU utilization, memory usage, request queue depth, and latency percentiles. The 99th percentile latency matters more than the average. Your users care about the worst cases, not the mean. I once had a model where the average latency was 120ms but the 99th percentile was 2.3 seconds due to occasional long sequences hitting the KV cache limit. The fix was increasing max-model-len and adding a pre-processing step to truncate excessively long inputs before they reached the inference engine.
When Inference Platforms Fail
No single platform handles every scenario. vLLM struggles with very large batch sizes on consumer-grade GPUs because memory becomes the bottleneck before compute does. If you need to serve hundreds of requests simultaneously on limited hardware, you'll hit the wall. The workaround is either adding more GPUs with tensor parallelism or switching to a platform like SambaNova that handles memory differently. Modal's serverless model breaks down when you have sustained high traffic. The cold-start penalty compounds across many requests, and the pricing model becomes expensive compared to reserved capacity. At that point, moving to self-hosted vLLM on provisioned GPUs is cheaper and faster. SambaNova and Cerebras require you to work within their model optimization pipelines. If you're using a custom architecture or a newer model they haven't supported yet, you're stuck waiting. The typical wait time for new model support is 2-4 weeks. For time-sensitive launches, that's a dealbreaker.
The reality is that inference infrastructure is not a solved problem. These companies have made genuine progress, but each has tradeoffs that matter depending on your specific constraints. The best approach is to benchmark your actual workload against multiple options before committing. Don't pick based on headlines or published benchmarks alone. Run the tests yourself with real data.
