Is vLLM ready for production in 2026?

Yes. Used by Databricks, Anthropic (for some workloads), Hugging Face Inference Endpoints. Active development, Apache licence. The "production-grade open LLM server" choice.

Should I bother with TensorRT-LLM?

Only if you are at >1000 concurrent users or need <50ms TTFT for trading/voice applications. Setup complexity 3-4× vLLM, performance gain 20-40%. For most teams, vLLM is the right answer.

Can I serve fine-tuned LoRA adapters on vLLM?

Yes — vLLM 0.5+ supports multi-LoRA serving. One base model + many adapters loaded at runtime, switched per request. Saves enormous VRAM versus loading multiple full fine-tunes.

Can I run a useful 70B model on consumer hardware?

Marginally. A single RTX 4090 (24 GB VRAM) cannot fit 70B in fp16. With 4-bit quantization (Q4_K_M GGUF in llama.cpp), Llama-3.3-70B fits in ~40 GB total memory; you can run it with CPU offload at 5-15 tok/s. Acceptable for personal use; not for production. If you need 70B production, budget A100/H100.

How does TensorRT-LLM compare to vLLM in 2026?

TensorRT-LLM (NVIDIA): higher peak throughput, NVIDIA-only, more complex to operate, best for fixed-model production at the largest scale. vLLM: easier to operate, model-agnostic (any HF transformer), 80-90% of TensorRT throughput, the standard choice for most teams. Pick TensorRT only if you have benchmarked vLLM and need the extra 10-20%.

Self-Host Llama/Mistral/Qwen: vLLM vs Ollama

Q: Can I serve fine-tuned LoRA adapters on vLLM?

Yes — vLLM 0.5+ supports multi-LoRA serving. One base model + many adapters loaded at runtime, switched per request. Saves enormous VRAM versus loading multiple full fine-tunes.

Q: Can I run a useful 70B model on consumer hardware?

Marginally. A single RTX 4090 (24 GB VRAM) cannot fit 70B in fp16. With 4-bit quantization (Q4_K_M GGUF in llama.cpp), Llama-3.3-70B fits in ~40 GB total memory; you can run it with CPU offload at 5-15 tok/s. Acceptable for personal use; not for production. If you need 70B production, budget A100/H100.

Q: How does TensorRT-LLM compare to vLLM in 2026?

TensorRT-LLM (NVIDIA): higher peak throughput, NVIDIA-only, more complex to operate, best for fixed-model production at the largest scale. vLLM: easier to operate, model-agnostic (any HF transformer), 80-90% of TensorRT throughput, the standard choice for most teams. Pick TensorRT only if you have benchmarked vLLM and need the extra 10-20%.

Read as

Three serious LLM runtimes, three different sweet spots. Ollama for developers and single-user. llama.cpp for edge and embedded. vLLM for production multi-user serving. This module benchmarks them on identical hardware, explains the architectural differences, and shows when to pick which.

You decided to self-host. Now you need to pick a runtime. The choice matters: 5× difference in throughput at scale, vastly different deployment patterns, security characteristics that differ. This module benchmarks Ollama, llama.cpp, and vLLM on the same model + hardware so you can pick based on your use case.

Architectural differences in one paragraph each

Ollama is a wrapper around llama.cpp with developer-friendly UX (model registry, single-binary install, REST API). Optimised for ease, not throughput. Single request at a time per model by default (no batching). llama.cpp is the underlying C++ inference engine. Supports GGUF format, runs on CPU, CUDA, Metal, ROCm, Vulkan. Maximum portability, minimum overhead. Used as embedding library by many other projects. vLLM is Berkeley research-grade server. Implements PagedAttention (KV-cache memory management) and continuous batching (different requests at different stages share GPU efficiently). Supports tensor parallelism for multi-GPU. Built for concurrent serving at scale.

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Self-Hosting Llama / Mistral / Qwen — vLLM vs Ollama vs llama.cpp Benchmarks

Architectural differences in one paragraph each

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Other modules in this track

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