Best Local LLMs for 24GB GPU in 2026 Compared

In 2026, running capable large language models (LLMs) locally on a single 24GB Graphics Processing Unit (GPU) is achievable, with modern 20B to 35B parameter models offering a practical balance between performance and VRAM usage. This approach supersedes older methods of squeezing larger, heavily quantized 70B parameter models onto consumer hardware. GPUs like the NVIDIA RTX 3090 or RTX 4090 fall within this 24GB VRAM tier, making them suitable for serious local inference tasks including coding, chat, and AI agents.

Three primary factors consume VRAM during LLM inference: model weights, the KV cache, and runtime overhead. Model weights' size is determined by parameter count and quantization level. For instance, a 32 billion parameter model quantized at Q4_K_M requires approximately 18-20GB for weights alone. Mixture-of-Experts (MoE) models, such as those in the Mixtral style, present a unique challenge as all experts remain in VRAM regardless of which ones are active for a given token, necessitating sizing based on total parameters rather than active ones.

The KV cache, which stores intermediate computations, grows with the context length of prompts and conversation history, directly impacting VRAM consumption. Runtime overhead from the serving stack typically adds another 1-2GB for shorter contexts. Achieving efficient use of 24GB VRAM hinges on effective quantization, with Q4_K_M serving as a standard for balancing model quality and memory footprint. Higher quantization levels like Q5_K_M and Q6_K improve quality but increase memory requirements, while Q8_0 and BF16 are generally too large for 30B-class models on a single 24GB card.

This guide evaluates models such as Qwen, Gemma, Mistral, and DeepSeek, detailing their suitability for 24GB VRAM configurations. It emphasizes how different quantization methods and model architectures impact VRAM allocation, providing insights into selecting the optimal LLM for local deployment. An interactive tool is available to dynamically adjust quantization settings and observe their effect on model fit within the 24GB VRAM constraint.