6 posts tagged with "Inference"

Text inference has a comfortable shape in llm-d. A request arrives as a few thousand tokens, prefill and decode run back to back on one replica, and a prefix-aware router keeps the KV cache warm. Every layer of the stack — scheduler, load balancer, autoscaler — is calibrated against that shape.

Then someone attaches an image, and it is tempting to think of the result as the same request with a picture bolted on. It is not. A multimodal request differs from a text-only one in almost every property a serving system actually reads: how big it is, how much it costs, how long it takes before the first output token, what identity its cache entries have, and how many stages it has to move through. The parts of the stack that were calibrated on text keep working, silently, on numbers that no longer mean what they used to.

This post walks those differences deliberately. First what makes media content different from text content. Then what that does to an inference request, difference by difference. Then, for each one, what llm-d actually does about it — the token estimator, the content-hash routing path, the retuned affinity gate, and the encode tier. Model server like vllm is the engine throughout; its per-replica mechanisms are the floor, not the answer.

Vision-language model (VLM) inference involves several stages: vision encoding, language model prefill, and language model decode. Each stage has different compute and memory characteristics, making heterogeneous disaggregation a natural fit. This gives us the flexibility to place different stages to different accelerator resources based on their compute and memory characteristics.

In this post, we evaluate a vision-heavy Kimi-VL serving workload on llm-d and SGLang. We place vision encoding stage in 4 Intel Arc Pro B60 (Intel B60) GPUs while keeping prefill and decode stage on 1 NVIDIA H200 GPU. Compared with the collocated baseline, the measured heterogeneous E/PD configuration achieved 2.4x-2.8x higher throughput and reduced mean Time To First Token (TTFT) by roughly 69%-80% under load.

For most of the transformer era, the KV cache rested on a quiet assumption: one model, one uniform cache. Every layer attended the same way, every block was the same size, and everything built on top of the cache (allocators, offload connectors, schedulers) could treat it as a single pool.

Hybrid models broke this assumption. Many recent frontier and open-weight models increasingly mix attention types within a single model (full attention next to sliding-window, linear, or Mamba layers), making the cache heterogeneous: different layers now hold different amounts of state, in different shapes, with different reuse rules. A cache block that used to be allocated as one uniform unit is now constituted of several distinct parts.

To serve a hybrid model efficiently, an AI inference platform has to handle that heterogeneity in at least three aspects of the stack:

• GPU Memory Allocation: How the cache is laid out and allocated on the GPU. vLLM solved this with its Hybrid Memory Allocator (HMA), rebuilt around a unified allocator (see Hybrid Models as First-Class Citizens in vLLM).
• KV Offloading: Extending the KV cache to CPU and storage. Without HMA awareness, an offloading connector turns the HMA off and therefore discards the GPU memory improvements or potential data movement savings.
• KV-Aware Routing: Sending each request to the right model-server replica. Ignoring hybrid memory structure may erroneously list nodes as having or not having the required KV data based on information stemming from just part of the layers.

vLLM's HMA solved hybrid GPU memory allocation when handling a single vLLM instance. This post shows how llm-d extends that to tiered KV cache management - including KV offloading to CPU and storage, and KV-aware request routing - significantly improving throughput and latency at scale for hybrid models.

Most production inference clusters today are single-vendor because that is often the simplest way to configure and operate a cluster.

That is starting to change. Procurement cycles bring new generations alongside older ones, supply planning spans multiple accelerator options, and cost/performance profiles differ by workload. Real production fleets are accumulating heterogeneity whether or not the architecture planned for it.

This is an opportunity to unlock real value: different accelerator classes can be matched to workload requirements, stranded capacity gets reclaimed, and operators gain more flexibility in capacity planning. The case is stronger still for sovereign and on-premise deployments, where data residency, regulatory alignment, and the long-term economics of high-volume inference make local fleet optimization especially important.

Making that work in practice is a non-trivial systems problem. Each accelerator stack brings its own optimized drivers, firmware, container images, runtime settings, and attention kernels. A coherent serving layer needs to preserve those platform-specific optimizations while still giving operators one control plane for routing, observability, and policy.

llm-d was born Kubernetes-native. Its workers are Deployments, its endpoints live in an InferencePool, and its guides assume a cluster is one kubectl away. That made sense: Kubernetes is where most production inference runs, and building on it gave llm-d a head start on networking, lifecycle, and scale.

But the thing that makes llm-d llm-d - KV-cache-aware scoring, prefix-cache affinity, prefill/decode disaggregation, flow control - was never fundamentally about Kubernetes. It is routing intelligence. It reasons about the state of a fleet of model servers and decides where each request should go. Nothing about that logic needs an API server. The dependency on Kubernetes was incidental, inherited from how endpoints happened to be discovered, not essential to what the router actually does.

This post is about pulling those two things apart. We introduce the EndpointDiscovery abstraction in the llm-d router that separates what endpoints exist from how to route across them, and the first plugin built on it - file discovery - which lets the full routing stack run as a plain process or container with no Kubernetes anywhere in sight: on an HPC cluster, inside a Ray job, on a bare-metal rack, or on your laptop.

Figure 1: The big picture - one routing stack under every platform. llm-d discovers endpoints through its EndpointDiscovery module (Kube Discovery against an InferencePool, File Discovery against everything else) and serves requests the same way on Kubernetes, Slurm, Ray, or bare metal (inference, HPC, and RL rollout workloads: veRL, SkyRL, prime-rl). The rest of this post explains how.

Not all LLM requests cost the same. A short prompt might complete in milliseconds, while a long one can occupy a GPU for seconds. If we can predict how long a request will take on each candidate server before dispatching it, we can make substantially better routing decisions. This post describes a system that does exactly that: a lightweight ML model trained online from live traffic that replaces manually tuned heuristic weights with direct latency predictions.