Run 24GB‑GPU LLMs Locally: Qwen, Gemma, Mistral, DeepSeek Ranked

Running large language models (LLMs).gold on a single 24GB GPU is now a realistic goal for researchers, hobbyists, and businesses alike. This article breaks down the top open‑weight models that fit into that VRAM window, their licensing, and the scenarios where each shines.

Why 24GB is the New Standard

When GPU budgets hit the 24GB mark, you unlock a sweet spot: enough memory for a full inference pipeline while keeping hardware costs manageable. 24GB cards like the Nvidia RTX 4090 or the AMD Radeon RX 7900 XTX provide the compute headroom for Q4_K_M quantization, which trims models to a fraction of their original size without sacrificing performance.

Model Showdown

Qwen 3.6

Qwen 3.6 from Alibaba’s AI Lab is a 1.5‑B parameter model that packs a punch in a single 24GB GPU. Its Q4_K_M implementation keeps VRAM usage around 20GB, leaving headroom for batch processing.

• **VRAM fit**: ~20GB with Q4_K_M quantization
• **License**: Apache‑2.0, open for commercial use
• **Best for**: Conversational agents and quick prototyping subscribed to low latency constraints
• **Strength**: Strong multilingual support across 100+ languages

Gemma 4

Gemma 4 by Google’s DeepMind is является a 2‑B parameter model that surprisingly fits on a 24GB GPU thanks to aggressive quantization. It offers a balance of speed and fidelity for text generation tasks.

• **VRAM fit**: ~18GB post‑quantization
• **License**: Apache‑2.0 with usage restrictions for competitive products
• **Best for**: Content creation, code completion, and data augmentation
• **Strength**: Excellent reasoning over complex prompts

Mistral Small

From Mistral AI, the Mistral Small model is a 1.3‑B parameter beast that’s a top choice when you need a lightweight yet capable LLM. It runs comfortably under 16GB, leaving extra memory for larger batch sizes.

• **VRAM fit**: ~14GB with Q4_K_M
• **License**: MIT, fully permissive
• **Best for**: On‑device inference in edge devices and low‑power servers
• **Strength**: Rapid inference, minimal latency

DeepSeek‑R1‑Distill

DeepSeek’s R1‑Distill compresses the flagship 20‑B model into a 3‑B checkpoint that fits on a 24GB GPU. This distillation technique preserves core capabilities while slashing memory.

• **VRAM fit**: ~22GB with Q4_K_M
• **License**: Open‑source under a custom license with commercial use allowed
• **Best for**: Enterprise chatbots and knowledge‑base retrieval systems
• **Strength**: High‑quality responses with domain‑specific tuning

gpt‑oss‑20B

The gpt‑oss‑20B from GPT‑Open Source is a 20‑B parameter model that, when quantized to Q4_K_M, lands just under 24GB. It’s ideal for those who want performance close to GPT‑3.5 without cloud dependencies.

• **VRAM fit**: ~23GB after quantization
• **License**: Apache‑2.0, fully permissive
• **Best for**: Research, large‑scale content generation, and data‑driven insights
• **Strength**: Broad knowledge base and robust text generation

Choosing the Right Fit

When selecting