GeForce RTX 4090 48GB - Modified Blower

VRAM holds the model and the working memory it needs while generating an answer. Compared with a 24GB card, 48GB gives you more room for model weights, longer conversations and larger workloads. The benefit is capacity: twice the memory does not mean twice the speed.

Check the exact model and quantisation, not just its parameter count. Lower-bit versions, such as 4-bit or 8-bit, reduce the space needed for model weights. Leave room for the context cache and runtime overhead too: longer prompts and parallel requests increase memory use. A model file smaller than 48GB is not, by itself, proof that the complete workload will fit.

These are common starting points for running local language models. Ollama lists the RTX 4090 among its supported NVIDIA GPUs, but that is not validation of this modified 48GB board. Confirm the driver, operating system and chosen runtime for the supplied card before committing to a deployment.

Yes, with a locally configured AI application. For example, LM Studio supports offline inference after the model and runtime have been downloaded. Use local models and local document processing, and check cloud features, external tools and integrations separately. Owning the GPU alone does not make every application private or offline.

Local inference is the main focus: running an existing model on your own workstation. Memory-efficient adaptation methods such as LoRA and QLoRA may also be suitable, depending on the model, sequence length, batch size and software. QLoRA uses quantised base weights with trainable adapters; it is different from full-model training. No specific fine-tuning configuration has been validated for this listing.

Two cards provide 48GB each, not one automatic 96GB memory pool. A compatible inference engine can split a model across GPUs; Ollama documents this approach when a model does not fit on one GPU. Your workstation still needs the PCIe slots, power, cooling and software support for both cards.