Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared

A single 24GB card is the practical floor for serious local inference. It is enough for genuinely capable models, and small enough to sit on one GPU. An RTX 3090 or RTX 4090 both land in this tier. The card you own matters less than the models you pick for it.

The old hobbyist move was to squeeze the biggest 70B quant onto the card. That advice is now outdated. The stronger 2026 strategy uses modern 20B–35B-class models that fit cleanly. These leave room for context, and still respond fast enough for coding, chat, and agents. This guide covers the models that actually fit, why each is worth running, and how 24GB gets spent.

Three things consume memory during inference. Getting the split right decides whether a model fits.

The first is model weights . Their size depends on parameter count and quantization. At Q4_K_M, a common home-inference default, each parameter costs roughly 0.58 bytes. A 32B model therefore needs about 18–20GB in weights alone. Mixtral-style Mixture-of-Experts (MoE) models are the common trap here. Every expert stays resident in VRAM even when only a few route per token. So you size MoE memory by total parameters, never active parameters.

The second is the KV cache , which grows with context length. Longer prompts and longer sessions eat more VRAM. The third is runtime overhead from the serving stack . A safe rule of thumb adds roughly 1–2GB for the KV cache and runtime at short context.

Quantization is the lever that makes 24GB workable. Q4_K_M is the standard balance of quality and footprint. Q5_K_M and Q6_K raise quality at a memory cost. Q8_0 and BF16 are usually too large for 30B-class models on a single 24GB card. The interactive tool below lets you switch quantization and see each model’s fit change live.

Every model below carries a permissive license, either Apache 2.0 or MIT. All of them fit a single 24GB card at Q4_K_M with room for context. They are grouped by the job each one does best.

Alibaba’s Qwen3.6-27B is the strongest single default for the card. It is a dense 27B model released in April 2026 under Apache 2.0. The release focuses on agentic coding, repository-level reasoning, and frontend workflows. At Q4_K_M it needs roughly 16GB, leaving comfortable headroom for context. Full model cards and the changelog sit in the Qwen3.6 GitHub repository .

The Qwen3.6-35B-A3B is a Mixture-of-Experts model with 35B total parameters and about 3B active per token. It decodes far faster than a dense 35B model because only a fraction of weights fire each step. The memory footprint still tracks total parameters, so plan for roughly 20GB at Q4_K_M. That makes it a tight but valid fit, best when you want speed for general chat and tool use.