Benchmarks

TL;DR Major enterprises are moving production AI workloads from GPT-4 to self-hosted Llama and Mistral models, achieving substantial cost reductions while maintaining acceptable quality for most use cases. This migration requires careful planning around API compatibility, prompt engineering adjustments, and performance validation. The typical migration path involves running both systems in parallel during a transition period, using an API compatibility layer that translates OpenAI-formatted requests to local model endpoints. Tools like LiteLLM and OpenAI-compatible servers in Ollama handle this translation, letting teams test self-hosted models without rewriting application code. ...

TL;DR llama.cpp supports heterogeneous multi-GPU configurations, letting you mix NVIDIA, AMD, and even Intel Arc cards in the same system for local LLM inference. Unlike Ollama’s automatic GPU detection, llama.cpp requires explicit layer distribution using the -ngl flag combined with --split-mode and --tensor-split parameters. This gives you fine-grained control over which layers run on which card, essential when mixing a high-VRAM card with lower-capacity GPUs. ...

TL;DR The OLLAMA_NUM_GPU environment variable controls how many GPUs Ollama uses for inference, but setting it correctly requires understanding your hardware topology and workload patterns. Unlike single-GPU setups where Ollama auto-detects your card, multi-GPU configurations demand explicit tuning to avoid memory fragmentation and PCIe bottlenecks. Set OLLAMA_NUM_GPU=2 to split model layers across two GPUs, or OLLAMA_NUM_GPU=4 for quad-GPU systems. Ollama distributes transformer layers sequentially – GPU 0 handles the first N layers, GPU 1 takes the next batch, and so on. This differs from data parallelism where each GPU processes different prompts simultaneously. ...

TL;DR Ollama Cloud offers managed hosting with zero infrastructure overhead, while local self-hosting gives you complete control and predictable costs after initial hardware investment. The decision hinges on your request volume, data sensitivity requirements, and whether you already own suitable hardware. For teams processing fewer than several thousand requests daily, Ollama Cloud eliminates the need to manage GPU servers, handle model updates, or troubleshoot CUDA driver conflicts. You pay per API call without worrying about idle capacity. Local hosting becomes cost-effective when you have consistent high-volume workloads that would generate substantial API bills – think continuous document processing pipelines or customer service chatbots handling hundreds of concurrent sessions. ...

TL;DR Running Qwen 3.5 locally requires choosing between Ollama’s CLI-first approach and LM Studio’s GUI-driven workflow. Both tools serve the same GGUF model files but differ significantly in performance characteristics and operational overhead. Ollama excels at automated deployments and scripting. Install with curl -fsSL https://ollama.com/install.sh | sh, pull the model using ollama pull qwen2.5-coder:7b, and start serving on port 11434. Memory usage stays consistent across inference requests, making it predictable for containerized environments. The CLI interface integrates cleanly with shell scripts and CI/CD pipelines. ...

TL;DR The RTX 3090 remains a compelling choice for local AI workloads in 2026, particularly on the used market where prices have stabilized considerably below launch MSRP. With 24GB of VRAM, this card handles most local LLM deployments that would otherwise require multiple newer cards or expensive cloud instances. On the secondary market, expect to find RTX 3090s from mining operations, workstation upgrades, and gamers moving to newer architectures. The key advantage is VRAM capacity – running a 70B parameter model quantized to 4-bit requires roughly 40GB, making dual RTX 3090s viable where a single RTX 4090 (24GB) falls short. For 13B to 34B models, a single card provides comfortable headroom. ...

RTX 4090 vs RTX 3090 for Local AI: Which GPU Should You Buy? TL;DR Both GPUs have 24GB VRAM, which is the most important spec for local AI. The RTX 4090 is 40-70% faster for inference but costs roughly twice as much as a used RTX 3090. For most people building a local AI server, the 3090 is the better buy. The 4090 makes sense only when you need maximum single-card speed or plan to do significant fine-tuning work. ...