Ollama Herd vs vLLM

What is vLLM?

vLLM (~93K GitHub stars as of September 2026) is a high-throughput LLM serving engine originally developed at UC Berkeley. It introduced PagedAttention for near-optimal GPU memory utilization and has become the default inference backend for serious NVIDIA GPU deployments. It also runs on AMD and Intel GPUs and on CPUs, and since September 2026 serves concurrent requests on Apple Silicon through the community-maintained vllm-metal plugin. vLLM supports continuous batching, tensor parallelism, speculative decoding, and multiple quantization formats.

What is Ollama Herd?

Ollama Herd is an open-source smart AI router that turns the Ollama and MLX servers on the machines you already own (Apple Silicon Macs, Linux servers, and Windows PCs, with or without NVIDIA GPUs) into one endpoint. It routes LLMs, embeddings, image generation, speech-to-text, and vision with 8-signal scoring, mDNS auto-discovery, and an 8-tab real-time dashboard. Apple Silicon Macs get extras: an MLX backend, native image generation, and speech-to-text. Two commands to set up, zero config files. pip install ollama-herd or brew install ollama-herd.

What vLLM Does

vLLM is a high-throughput LLM serving engine from UC Berkeley that transformed how inference engines manage GPU memory. Core capabilities:

• PagedAttention: Manages KV cache memory like virtual memory pages, eliminates fragmentation, enables near-optimal GPU memory utilization. This is vLLM's signature innovation.
• Continuous batching: Dynamically adds new requests to running batches without waiting for the current batch to complete. Dramatically improves throughput under concurrent load.
• Tensor and pipeline parallelism: Tensor parallelism splits the math inside each layer across GPUs, usually within one machine; pipeline parallelism gives each GPU or machine a different slice of the layers. Together they run 70B+ models across 2-8 GPUs or several servers.
• Speculative decoding: Uses a smaller draft model to predict tokens, then verifies in parallel with the main model. Up to 2-3x faster generation.
• Quantization support: AWQ, GPTQ, FP8, and more. Run larger models in less VRAM with minimal quality loss.

Feature Comparison

Where vLLM Wins

1. Raw throughput on NVIDIA GPUs. On an H100 or A100, vLLM's continuous batching + PagedAttention delivers throughput that Apple Silicon cannot match. If you're serving 1,000 concurrent users, vLLM on proper hardware is the clear choice.
2. PagedAttention efficiency. vLLM's KV cache management is genuinely best-in-class. Near-zero memory waste means you can serve larger models or more concurrent requests than naive implementations allow.
3. Continuous batching. Dynamically interleaving requests without batch boundaries is essential for production serving at scale. Herd doesn't batch, it routes individual requests to nodes.
4. Large model support. Tensor parallelism across 4-8 GPUs lets you serve 70B-405B parameter models at production speeds. A single Mac holds whatever fits in its unified memory (a 512GB Mac Studio fits 400B-class models), but it serves them to a handful of users, not thousands.
5. Enterprise and cloud deployments. vLLM powers model serving at Anyscale, is integrated into serving platforms such as Ray Serve and BentoML, and has extensive production deployment documentation.
6. Speculative decoding. Draft-model acceleration gives meaningful speedups for interactive use cases. This is a serving optimization Herd doesn't do (it optimizes routing, not serving).
7. Ecosystem and integrations. Prometheus metrics, structured output, LoRA adapter hot-swapping, prefix caching, deep features built for production ML workloads.

Where Ollama Herd Wins

1. Multi-device fleet routing. vLLM optimizes one server (or one GPU cluster). Herd orchestrates an entire fleet of heterogeneous devices, MacBooks, a Mac Studio, a Linux box, a Windows gaming PC, routing each request to the best available node.
2. Zero configuration. Run herd on one machine and herd-node on the others; mDNS finds them with no config files. vLLM requires careful GPU memory configuration, batch size tuning, model-specific flags, and infrastructure setup.
3. Built for hardware you already own. Macs need no CUDA; Linux and Windows machines, NVIDIA or not, join through Ollama. No GPU servers, no cloud spend. A team of 5 developers with MacBook Pros has a meaningful AI fleet already sitting on their desks.
4. Multimodal routing. Five model types (LLMs, embeddings, image generation, speech-to-text, and vision) with type-aware routing. vLLM focuses on text generation, with embeddings and transcription endpoints, and does not do image generation.
5. Intelligent routing with capacity learning. 8-signal scoring (whether the model is already warm, memory fit, queue depth, estimated wait, role affinity, availability trend, context fit, and session affinity) adapts over time. Herd learns which node performs best for which model and routes accordingly.
6. Operational visibility. 8-tab dashboard showing fleet health, model distribution, routing decisions, and benchmark results, without setting up Prometheus, Grafana, or any monitoring stack.
7. Meeting and availability awareness. Detects when a Mac is on a video call (macOS) and learns when each machine is usually busy, and routes traffic away. These are consumer-hardware realities that server-focused tools don't consider.
8. Ollama ecosystem. Access the full Ollama model library, pull any model, it's immediately routable. No model conversion, no format compatibility issues, no serving configuration per model.
9. Cost. A fleet of machines you already own has no per-token or rental cost, only electricity. A single A100 cloud instance costs $1-3/hour. For small teams, the economics aren't close.

The Core Difference

vLLM and Ollama Herd operate at fundamentally different layers:

• vLLM is a serving engine. It takes one model on one machine (or GPU cluster) and serves it as efficiently as possible, maximizing tokens per second through memory optimization and batching.
• Ollama Herd is a routing layer. It takes many models across many machines and routes each request to the best available node, maximizing fleet utilization through intelligent scheduling.

Conceptual: where each tool sits (a tool can span layers)

Clientschat UIs, coding agents, apps

Open WebUI Claude Code Codex Your apps and agents

Gateway / edgeauth, rate limits, cloud providers

LiteLLM Bifrost Envoy AI Gateway HAProxy

Fleet routerpicks one machine per request

Ollama Herd Olla NVIDIA PAIR LoLLMs Hub ollama_load_balancer OLOL LocalAI (distributed mode) GPUStack (cluster manager) llama-swap (peers) LiteLLM (Ollama load balancing)

Inference serverruns the model on one machine

Ollama mlx_lm.server vLLM LocalAI llama-swap Docker Model Runner LM Studio llama.cpp server NVIDIA PAIR (manages engines)

One model, many machinessplits a model too big for one

exo MLX distributed LocalAI (worker mode) OLOL (sharding) vLLM (multi-node)

Hardwarethe machines themselves

Macs Linux servers Windows PCs NVIDIA GPU servers

Running Both

Herd does not route to vLLM. Herd nodes serve Ollama and MLX (mlx-lm) models, so a vLLM server can't join a Herd fleet as a node today. The two still work well side by side as separate endpoints:

Example setup: two endpoints, side by side

Ollama Herd :11435

Interactive, multimodal, and agent traffic

Clientschat, agents, embeddings, images

base URL :11435

Herd routerscores the nodes

each request goes to one node

Mac #1Ollama

Mac #2MLX

Linux boxOllama

vLLM :8000

High-concurrency batch jobs for one big model

Batch clientsmany requests, one model

base URL :8000

vLLMcontinuous batching, PagedAttention

one server

GPU serverone model, high concurrency

Point interactive, multimodal, and agent traffic at Herd, and point high-concurrency batch jobs for one big model at vLLM. Both speak the OpenAI API, so switching a client between them is a base-URL change.

When to Choose Each

Bottom Line

vLLM is the best LLM serving engine for NVIDIA GPUs, full stop. If you have A100s or H100s and need to serve models at scale, vLLM is the right tool. It's battle-tested, widely deployed, and continuously improving.

Ollama Herd is a fleet routing layer for the machines you already own. It doesn't try to serve models, it trusts Ollama (and MLX on Macs) for that. What it does is make a collection of Macs, Linux boxes, and Windows PCs act as one intelligent AI system, routing the right request to the right machine at the right time.

The typical vLLM user is an ML engineer managing GPU servers in a data center. The typical Herd user is a developer or small team with 3-8 machines who want local AI without cloud costs. These audiences barely overlap today, but as local AI grows, running GPU servers and desktop fleets side by side becomes increasingly common.

Getting Started

pip install ollama-herd    # or: brew install ollama-herd
herd                       # start the router
herd-node                  # on each device

Running vLLM on GPU servers too? Keep it as its own endpoint (see Running Both); Herd handles the fleet.

Frequently Asked Questions

Is Ollama Herd a good alternative to vLLM?

They serve different purposes. vLLM maximizes throughput on NVIDIA GPUs for high-concurrency production workloads. Ollama Herd maximizes utilization across a fleet of the machines you already own (Macs, Linux boxes, Windows PCs) with zero configuration. If you have GPU servers and need to serve thousands of concurrent users, choose vLLM. If you have several machines and want them working together as one AI system, choose Herd.

Can I use Ollama Herd with vLLM?

Side by side, yes. As a Herd backend, not today: Herd nodes serve Ollama and MLX (mlx-lm) models, and Herd does not route to vLLM endpoints. Run vLLM on a GPU server next to your Herd fleet and point each client at the endpoint that fits the workload.

How does vLLM compare to Ollama Herd for team use?

vLLM is designed for ML engineering teams managing GPU infrastructure in data centers. Ollama Herd is designed for developer teams with 3-8 machines they already own who want shared local AI without cloud costs. vLLM needs a hardware backend (CUDA, ROCm, XPU, CPU, or vllm-metal on Apple Silicon), memory and batch tuning, and infrastructure expertise. Herd requires two commands and zero configuration.

Does Ollama Herd require CUDA or NVIDIA GPUs?

No. Herd routes to Ollama and MLX, so Apple Silicon Macs need no CUDA or NVIDIA drivers. Nodes can also be Linux or Windows machines running Ollama, including machines with NVIDIA GPUs.

Is Ollama Herd free?

Yes. Open-source, MIT license. No paid tiers, no API keys, no subscriptions.

See Also

• Ollama Herd vs exo, Peer-to-peer model splitting vs fleet routing
• Ollama Herd vs GPUStack, GPU cluster management vs local fleet routing
• Ollama Herd vs LocalAI, A multi-backend engine that clusters its own nodes