vLLM vs TGI vs llama.cpp for Production LLM Serving
The model is only half the procurement decision. The serving engine that loads the weights, schedules tokens, and exposes an HTTPS endpoint determines how many concurrent users a single GPU can carry, how predictable latency stays under load, whether structured outputs are reliable, and whether a quiet edge box without a server-class GPU is a viable Hosn deployment at all. Three open-source engines dominate sovereign-grade deployments in 2026: vLLM, Hugging Face TGI, and llama.cpp. They are not interchangeable. Picking the wrong one wastes hardware budget and corners the operations team into rebuilds.
The serving-stack decision, before the model decision
A frequent procurement mistake is to pick the model first (Gemma 4, Qwen 3.6, DeepSeek R1) and inherit whichever engine the integrator happens to know. The better order is to characterise the workload, then pick the engine, then load whichever supported model fits. The four workload axes that matter are concurrency (how many simultaneous users at peak), output shape (free-form chat versus strict JSON or grammar-constrained extraction), hardware tier (multi-GPU rack, single-GPU tower, CPU or Apple-Silicon edge), and operational posture (continuous patching versus rare touch). Each engine sits on a clear point in that space. The rest of this article walks the three with sovereign-buyer eyes, then closes with a Hosn-tier matrix. For the broader model-side framing, see the pillar piece on Gemma 4 256K context.
vLLM, the throughput leader for GPU racks
vLLM is the reference open-source engine for high-throughput GPU serving. Its core innovation is PagedAttention, an attention algorithm inspired by virtual memory paging in operating systems. Where naive serving allocates a contiguous KV cache buffer per request and burns memory to internal and external fragmentation, PagedAttention stores the cache in fixed-size blocks and assembles them on demand, achieving near-zero waste and enabling block-level sharing across requests. The original SOSP 2023 paper, Efficient Memory Management for Large Language Model Serving with PagedAttention, reports 2x to 4x higher throughput than FasterTransformer and Orca at matched latency, with 8.5x to 15x advantages in parallel-completion scenarios.
Combined with continuous batching (the scheduler admits new requests on every step instead of waiting for a fixed batch to finish), the practical effect is that a vLLM-served GPU keeps far more concurrent users at the same per-token latency. For a sovereign rack hosting an institutional assistant, that is the difference between four GPUs and twelve. The vLLM documentation covers tensor parallelism, prefix caching, speculative decoding, and quantised weight formats, and the engine has become the default backend behind several commercial serving products. Limitations are honest: vLLM is GPU-only (CUDA, with experimental ROCm and TPU support), the configuration surface is broad, and structured-output features lag TGI's grammar engine in maturity.
TGI, Hugging Face's production-grade serving with structured-output strength
Hugging Face TGI is the engine that powers Inference Endpoints and the bulk of Hub-served deployments. It supports the same throughput-oriented techniques (continuous batching, FlashAttention, tensor parallelism, paged-style KV management in recent releases) and adds a stronger guided-decoding stack: per-request JSON schema enforcement, regex grammars, and tool-use scaffolding that keep outputs on-grammar without round-tripping through a parser. The official TGI documentation describes the feature set across Llama, Qwen, Gemma, Falcon, and DeepSeek families.
Where TGI shines for sovereign use is the structured-extraction queue: clinical scribe, KYC field extraction, document classification, regulator-template form-filling, audit field tagging. These jobs do not need maximum chat throughput; they need every output to validate, every time. TGI's grammar layer cuts post-processing failures dramatically. It also slots cleanly into Hugging Face's broader operational stack (model card signing, dataset lineage, evaluation harness), which sovereign procurement teams find easier to audit. On a single-GPU Tower with a steady extraction workload, TGI is often the right pick over vLLM.
llama.cpp, the CPU-friendly edge engine
llama.cpp is the open-source C/C++ implementation that took LLM inference off server racks and onto laptops, phones, and Apple Silicon desktops. The project repository ships with no external dependencies, supports CPU SIMD extensions (AVX-512, AVX2, ARM NEON), Apple Metal, CUDA, ROCm, and Vulkan, and reads the GGUF quantisation format that has become the de facto standard for distributing reduced-precision open-weight models (1.5-bit through 8-bit). Its bundled llama-server exposes an OpenAI-compatible HTTP endpoint, which means application code written against vLLM or TGI usually works without changes.
For a sovereign edge deployment, llama.cpp is often the only viable option. A Mac Studio M3 Ultra with 256 GB unified memory runs Gemma 4 32B or Qwen 3.6 72B at 4-bit GGUF quantisation with usable latency for a single user, no fans, and a quiet office desk as the rack. A field office with a small Strix Halo workstation or a ruggedised x86 box gets the same story. llama.cpp will not match a vLLM rack on aggregate throughput, but for one-to-five-user assistants in a discreet location it is the right answer.
The Hosn-tier matrix
Hosn aligns the three engines with the three appliance tiers so the operations team carries one stack per tier and the buyer sees one decision per use case.
- Hosn Rack (multi-GPU, ~50-500 concurrent users): vLLM as the default chat and assistant engine, optionally with a TGI sidecar for structured-output queues. The router routes by response schema.
- Hosn Tower (single-GPU, ~5-50 concurrent users): vLLM if chat-dominant; TGI if extraction-dominant. Mixed loads can run both engines on the same GPU through partitioned VRAM, with the router as gatekeeper.
- Hosn Kernel (CPU or Mac Studio M3 edge, ~1-5 users): llama.cpp with GGUF-quantised models. The same OpenAI-compatible endpoint as the larger tiers, so client code is portable end-to-end.
For institutions that mix tiers (a Rack at HQ, Towers in major branches, Kernel boxes at field sites), the routing layer in front keeps the experience consistent. An analyst's prompt in a remote office hits llama.cpp on a quiet Mac Studio; the same analyst connecting at HQ hits vLLM on a rack. The model identity, the API surface, and the audit trail look identical from the application side. Sizing across these tiers is covered in sovereign-AI appliance sizing for users and latency.
Hosn ships, patches, and operates all three engines under a single SLA. To map your workload to a tier, email [email protected] for a one-hour briefing.
Frequently asked
Is vLLM always the right default for a sovereign appliance?
For a multi-GPU rack carrying concurrent users, yes. PagedAttention plus continuous batching delivers 2x to 4x more throughput than naive HuggingFace serving at equivalent latency, and the project is the de facto reference for open-weight production deployments. On a single-GPU Tower with a structured-output-heavy workload, TGI is often a better fit. On a CPU-only or Apple-Silicon edge box, vLLM is the wrong tool entirely and llama.cpp wins.
Why does llama.cpp matter when GPUs are cheaper than ever?
Three reasons. First, sovereign edge sites (a remote ministry office, a field clinic, a small classified room) often cannot host a server-class GPU and instead need a quiet, low-power box. Second, Apple Silicon unified memory makes a Mac Studio M3 Ultra surprisingly capable for a single-user assistant on Gemma 4 or Qwen 3.6 quantised to 4-bit. Third, GGUF quantisation lets institutions test and pilot a model without committing to a Tower-class accelerator at all.
Can the same Hosn appliance run vLLM and TGI side by side?
Yes. The Hosn Rack and Tower tiers ship with a router in front of the engines, and a single appliance can host vLLM for high-throughput chat and assistant traffic while TGI serves a structured-extraction queue (clinical scribe, KYC, document classification). The router decides per request based on response schema and latency budget. Operators see one HTTPS endpoint and one observability stack.
Does the choice of engine affect compliance posture?
Not directly. All three engines are open-source, run on-premise, and emit no outbound telemetry by default once the weights are local. The compliance question is operational: who patches the engine, who reviews CVEs, who maintains the GPU driver stack, who keeps the model registry signed. Hosn handles all of that under a single SLA across vLLM, TGI, and llama.cpp on every tier.