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

A single 24GB card is a viable floor for serious local projections. It's enough for truly capable models, and it's small enough to sit on a single GPU. RTX 3090 or RTX 4090 both stay at this level. The card you own is important under the models you choose.

An old hobby move was to squeeze a large amount of 70B into a card. That advice is out of date. A solid 2026 strategy uses modern models of the 20B–35B category that fit cleanly. This leaves room for context, and is still responsive enough for coding, chat, and agents. This guide covers which models are really suitable, why each should work, and how to use 24GB.

How to use 24GB of VRAM

Three things consume memory during prediction. Getting the classification right determines whether the model fits.

I first the model weights. Their size depends on the parameter calculation and the value expansion. For Q4_K_M, the home inference default, each parameter costs about 0.58 bytes. So the 32B model needs about 18–20GB in weights alone. Mixtral-style Mixture-of-Experts (MoE) models are a common pitfall here. Every master resides in VRAM even if there are only a few threads per token. So the MoE memory size with value parameters, invalid parameters.

I second is the KV cachewhich increases with the length of the core. Long updates and long sessions consume more VRAM. I the third is the runtime overhead of the deployment stack. A safe rule of thumb is to add about 1–2GB of KV cache and runtime for a short context.

Quantization is the lever that makes 24GB work. Q4_K_M is a general balance of quality and printing. Q5_K_M and Q6_K increase the quality with the amount of memory. The Q8_0 and BF16 are typically the largest of the 30B class models on a single 24GB card. The handy tool below allows you to change the measurement and see the change of each model live.

Six best local LLMs for 24GB GPU

All models below are licensed under the Apache 2.0 or MIT license. All fit one 24GB card in the Q4_K_M with core space. They are collected for each job they do best.

Qwen3.6-27B — best-around all and agent code

Qwen3.6-27B of Alibaba has one automatic power on the card. It is a compact model of 27B released in April 2026 under Apache 2.0. The release focuses on agent documentation, database-level reasoning, and front-end workflows. For Q4_K_M it needs about 16GB, leaving a comfortable headroom for context. The full model cards and changelog live in the Qwen3.6 GitHub repository.

Qwen3.6-35B-A3B — the fastest general purpose option

Qwen3.6-35B-A3B is a Mixture-of-Experts model with 35B total parameters and about 3B active per token. It cuts much faster than the compact 35B model because only a fraction of the weights are fired per step. The memory footprint still tracks the absolute parameters, so plan around 20GB for Q4_K_M. That makes it balanced but valid, best if you want normal chat speed and tool usage.

Gemma 4 26B — many ways and many languages

Google DeepMind released Gemma 4 on April 2, 2026, under Apache 2.0. It was the first generation of Gemma to ship with a fully open license, on the DeepMind Gemma page. The family consists of edge models, a 12B integrated multimodal model, a 26B MoE with 3.8B active, and a larger dense flag. The 26B MoE is a natural 24GB choice if you need vision input and 140+ language input.

Mistral Small 3.2 24B – polished everyday helper

The Mistral Mini Line targets the daily assistant's workload with low latency. Mistral Small 3.1 added multimodal input and a long context window, as well as refined Small 3.2 instructions. The densest model is 24B under Apache 2.0, and the lightest footprint in this list is about 14GB in Q4_K_M. That headroom allows you to push the core further than the heavier 32B options allow. In 2026 Mistral sent out some great minions again, but those stayed above the single card category.

gpt-oss-20b – simple reverse logic

OpenAI's gpt-oss-20b is an open-source logic model under Apache 2.0. Mixture-of-Experts design with 21B total parameters and 3.6B active per token. It ships in native MXFP4 4-bit format, loads around 14GB with plenty of headroom. It is strong in systematic thinking and the use of tools, and weak in general knowledge of the world.

DeepSeek-R1-Distill-Qwen-32B – deep thinking, strong balance

DeepSeek has broken down its R1 imaging probes into smaller compact models. DeepSeek-R1-Distill-Qwen-32B is a variant of 32B, built on the basis of Qwen2.5 and released under the MIT license. For Q4_K_M it uses about 18–20GB, which is the most suitable for this guide. It exposes its chain of thought through visual thought tokens, useful for slow, deliberate problems. Ready-made GGUF builds are available from bartowski at Hugging Face.

What doesn't fit in 24GB

Open models for the 2026 frontier are large MoE systems that are empty. They are great in functionality and concept, but they don't apply to a single consumer card. GLM-5.2 from Z.ai is about 753B-total MoE. Moonshot's Kimi K2.7 is about 1T total. DeepSeek sent the V4 as a public preview in April 2026, with the V4-Pro test site near the 1.6T limits. Alibaba's Qwen3.5-397B and Mistral Large 3 remain in the same server-class range.

Because the MoE memory keeps track of the total parameters, all of this requires multi-GPU rigs or high-memory integrated systems. They are worth knowing as API options. They do not work on a single 24GB card.

How are these models used in the environment

The three run times cover almost all of the setup. Ollama is a simple method, with automatic measurement selection and OpenAI-API compatibility. llama.cpp provides fine control over GGUF sizing and output. vLLM is a server focused on processing heavy concurrent workloads.

Start with a single model that matches your main project, not the biggest file you can upload. Keep the context under control, and let the card do what it does best. Run AI for a sensitive environment without sending a single token to cloud storage.

Key Takeaways

• 24GB is a practical floor: use the right-sized 20B–35B models, not the massive 70B you can't fit into.
• Qwen3.6-27B has one automatic power; DeepSeek-R1-Distill-Qwen-32B is roughly around ~18–20GB.
• MoE memory tracks value parameters, so every expert stays even if it's a small route per token.
• Quantization is an equal measurer: Q4_K_M is a home base; Q8_0 and BF16 rarely fit a 30B model on the same card.
• GLM-5.2, Kimi K2.7, DeepSeek V4, and Mistral Large 3 are server-class and will not run on a single 24GB GPU.