Local LLM Inference Stack 2026: vLLM, Ollama or LM Studio?

"The Local LLM space in 2026 is where Kubernetes was in 2017. Lots of tools. Confusing landscape." That is how Saiyam Pathak characterised it on X this week, and it is an accurate description.

Five frameworks dominate team discussions around local AI deployment: vLLM, SGLang, TensorRT-LLM, Ollama, and LM Studio. Each solves a different problem. Pick the wrong one and you either leave significant performance on the table, or spend weeks over-engineering infrastructure for a workload that a simpler tool would have served just fine.

Practitioners mapping the local LLM tooling ecosystem on X this week grouped the main serving solutions by purpose. This article translates that categorisation into concrete selection guidance for SMBs and engineering teams.

The Five Core Frameworks

vLLM, the production default

vLLM has become the de facto standard for teams deploying a local model as an OpenAI-compatible REST API. Its core differentiators: continuous batching (multiple requests served in parallel without blocking), PagedAttention for efficient GPU memory management, and straightforward integration with Python backends via its native OpenAI-compatible interface.

For a team deploying an internal document chatbot, an AI-assisted communications tool, or a shared company assistant, vLLM reaches a production-grade endpoint faster than most alternatives. Community-reported throughput for models such as Qwen2.5-72B on a dual-GPU server runs 30-80 tokens per second, depending on batch size and quantisation level.

vLLM also integrates cleanly with Ray for distributed inference across multiple GPUs, useful when a single card cannot hold your chosen model at the required quality level.

SGLang, when throughput is the constraint

SGLang introduces RadixAttention, a prefix-caching mechanism that avoids redundant computation when many requests share the same system prompt. In enterprise deployments where a fixed company-wide system prompt precedes every query, this matters: the shared prefix is computed once and cached, not recomputed per request.

If your workload involves high-volume processing of similar documents, contracts, invoices, support tickets, compliance reports, it is worth benchmarking SGLang against vLLM for your specific scenario. Community measurements report 20-40% higher throughput in batch scenarios with long shared prefixes, as reported by practitioners on r/LocalLLaMA, though results vary by model architecture and hardware.

TensorRT-LLM, NVIDIA hardware at full capacity

NVIDIA's own inference runtime extracts maximum performance from NVIDIA GPUs: FP8 and INT4 quantisation, speculative decoding, fused attention kernels, and hardware-specific optimisations for H100, A100, and the DGX platform. Setup is more involved than Ollama or vLLM, but the performance ceiling is meaningfully higher on dedicated NVIDIA hardware.

Organisations that have already invested in DGX Spark clusters, covered in detail in our June 28 cluster setup guide, should evaluate TensorRT-LLM as their production serving backend once basic cluster connectivity is confirmed.

Ollama, zero to running in under two minutes

Ollama is the entry point for anyone who wants a local model running immediately with no configuration overhead. A single command (ollama pull llama3.3:70b) downloads a quantised model and starts a local API server compatible with the OpenAI SDK. Over 170 ready-to-run models are available, including Qwen3, Gemma 4, Llama 3.3, DeepSeek-V3, and Mistral.

For the development and validation phase of a local AI pilot project, Ollama is the natural first choice: minimal setup, no prerequisite infrastructure, runs identically on Mac, Linux, and Windows. Concurrent request handling hits its limits sooner than vLLM for multi-user production loads, that is the inflection point where graduating to a dedicated serving engine pays off.

LM Studio, the GUI option for non-technical teams

LM Studio wraps local model inference in a desktop interface: a model browser, chat UI, and a local API server that can be exposed to other tools. It is notably well-optimised for Apple Silicon, practitioners report token rates on M-series Macs comparable to or exceeding llama.cpp with equivalent quantisation.

For organisations where most users are not comfortable with terminal commands, LM Studio substantially lowers the barrier: models are installed through a browseable catalogue rather than shell commands, and the chat interface is immediately usable without any configuration. The local API endpoint it exposes allows integration with other applications while keeping the client experience non-technical.

Choosing by scenario

The right framework depends primarily on usage pattern and available hardware:

• Individual developer, MacBook or Mac Studio: Ollama to start; LM Studio as a GUI-first alternative
• Team server for 5-30 internal users (GPU server): vLLM behind an OpenAI-compatible endpoint
• High-volume batch document processing: SGLang for prefix-caching efficiency
• NVIDIA enterprise hardware (DGX, H100, A100): TensorRT-LLM for maximum throughput
• Non-technical business team on Mac: LM Studio

The practical path for most SMBs: begin with Ollama on existing hardware, validate the use case and estimate concurrent users, then migrate to vLLM or SGLang when production load justifies the additional setup complexity. This avoids the risk of over-engineering infrastructure before the value is proven.

What this means for European businesses

As Saiyam Pathak noted this week, "best practices still emerging. Early adopters compounding advantage." That is the strategic argument for starting now rather than waiting for the ecosystem to consolidate around a single dominant approach.

The business case for local deployment is largely the same regardless of which framework you choose:

• GDPR compliance: All data stays on your hardware, no Article 44 third-country transfer, no data processing agreements required with cloud infrastructure providers
• No per-token costs: Fixed infrastructure costs replace variable API billing, community calculations suggest 20-100× cheaper at high volume for workloads with consistent query patterns
• EU AI Act auditability: As a deployer, you maintain full control over the model, system prompt, and configuration, auditable at any time, satisfying the documentation obligations that apply under the Act's deployer provisions

Our data sovereignty guide explains how local inference maps to specific GDPR obligations in practical terms. For teams unsure which framework and which model fit their use case, our training programme covers framework selection and initial deployment end-to-end, or reach out directly and we can scope the right stack for your requirements.