Best Local LLMs for a Single 24GB GPU: Qwen, Gemma, Mistral and DeepSeek Compared

Why Running Local LLMs on a Single GPU Is Now a Serious Option

The question of which local LLMs run best on a single 24GB GPU has never been more relevant. For developers, privacy professionals, and IT decision makers who want to keep sensitive data off commercial cloud infrastructure, the 24GB VRAM threshold represents the practical entry point for running powerful, capable language models entirely on-premises — with no data leaving the building. A new comparative guide published by MarkTechPost evaluates six open-weight models that comfortably fit on a single 24GB card using the Q4_K_M quantisation format, making genuinely capable AI inference accessible without enterprise-grade hardware budgets.

This matters enormously in the European context. With GDPR enforcement tightening, the EU AI Act entering its implementation phases, and growing institutional pressure to achieve digital sovereignty, the ability to run a high-performing language model on a single workstation GPU — without routing queries through OpenAI, Anthropic, or Google's servers — has shifted from a hobbyist curiosity to a legitimate enterprise strategy. According to research published by the European Union Agency for Cybersecurity (ENISA), data residency and processing location remain among the top concerns for organisations handling personal or sensitive information under EU law. Local inference directly addresses those concerns.

The models under review — Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, and DeepSeek-R1-Distill — each occupy a distinct niche. Understanding which fits your workflow requires evaluating not just raw performance, but licensing restrictions, VRAM headroom at Q4_K_M quantisation, and the specific tasks each model handles best. This guide synthesises those dimensions for practitioners who need to make informed, defensible decisions.

What the 24GB VRAM Floor Actually Means for Self-Hosted AI

VRAM capacity is the single most important hardware constraint when running large language models locally. The 24GB tier — represented by cards such as the NVIDIA RTX 3090, RTX 4090, and the professional-grade RTX 6000 Ada — has emerged as the sweet spot where model capability and hardware affordability intersect. Below this threshold, model quality degrades noticeably or context windows shrink to impractical lengths. Above it, you're entering multi-GPU or data-centre territory that most small teams and individual practitioners cannot justify.

Quantisation is the mechanism that makes 24GB viable. Q4_K_M, a 4-bit quantisation format popularised by the llama.cpp ecosystem, reduces model weights to approximately half the memory footprint of a full 16-bit (fp16) model, with quality degradation that is measurable in benchmarks but largely imperceptible in practical use. A model that would require 40GB in full precision can run comfortably in 20–22GB at Q4_K_M, leaving headroom for the key-value cache that handles context. As noted by the Hugging Face documentation and community benchmarks, Q4_K_M consistently outperforms simpler 4-bit schemes in perplexity metrics, making it the default recommendation for single-GPU deployment.

The practical implication for GDPR compliance is significant. When inference runs locally, personal data processed by the model — customer queries, internal documents, healthcare records, legal correspondence — never transits to a third-party processor. This eliminates an entire category of data processing agreements, cross-border transfer assessments, and vendor risk management obligations. For organisations operating under Article 28 of the GDPR, that simplification alone can justify the hardware investment.

Six Open-Weight Models for Local Inference: Strengths, Licensing and VRAM Fit

The six models evaluated each bring a distinct profile. Here is a structured breakdown of what practitioners need to know before deploying any of them in a production or near-production environment.

Qwen3.6 from Alibaba Cloud ships under the permissive Apache 2.0 licence, meaning it can be used freely in commercial applications without royalty obligations. Its multilingual capabilities — covering dozens of languages with strong performance on non-English benchmarks — make it a natural fit for European organisations that need to process documents in German, French, Spanish, Dutch, or other EU languages. Community benchmarks on Hugging Face's Open LLM Leaderboard consistently place Qwen-series models among the top performers in their parameter class.

Gemma 4, developed by Google DeepMind, offers strong general-purpose instruction following and performs well on document summarisation tasks. However, practitioners should note that the Gemma Terms of Use impose restrictions that differ from fully open licences — commercial use is permitted but subject to conditions that require careful review before enterprise deployment, particularly for organisations reselling AI-powered services.

Mistral Small is arguably the model with the strongest European provenance in this list, developed by Paris-based Mistral AI — a company that has positioned itself explicitly as a European alternative to US and Chinese AI labs. Available under Apache 2.0, Mistral Small excels in retrieval-augmented generation (RAG) architectures and enterprise chat deployments. According to Mistral AI's published technical documentation, the model is optimised for low-latency inference, which translates to responsive performance even on a single consumer GPU.

gpt-oss-20b represents OpenAI's foray into open-weight model distribution. At 20 billion parameters, it fits within the 24GB envelope at Q4_K_M and performs strongly on instruction-following and agentic task frameworks. The licence, however, warrants scrutiny: OpenAI's open-weight releases typically carry usage restrictions that differ materially from Apache 2.0 or MIT, and organisations deploying in regulated environments should conduct a thorough licence review before relying on this model in production workflows.

DeepSeek-R1-Distill rounds out the comparison with a focus on chain-of-thought reasoning and mathematical problem-solving. Released under the MIT licence — the most permissive option in this group — it is an attractive choice for technical teams building applications that require structured logical reasoning, scientific computation, or step-by-step problem decomposition. DeepSeek's rapid rise in the open-weight community has been well-documented, including coverage in Wired and MIT Technology Review, which noted the model family's surprisingly strong performance relative to its compute footprint.

How These Models Compare on Key Capabilities

For teams that need to prioritise quickly, the following chart maps each model's relative strength across the most common enterprise use cases. Scores reflect community consensus from benchmark aggregators and practitioner evaluations rather than any single test.