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

A single 24GB card is the sensible ground for critical native inference. It is sufficient for genuinely succesful fashions, and sufficiently small to sit down on one GPU. An RTX 3090 or RTX 4090 each land in this tier. The card you personal issues lower than the fashions you decide for it.

The previous hobbyist transfer was to squeeze the most important 70B quant onto the cardboard. That recommendation is now outdated. The stronger 2026 technique makes use of trendy 20B–35B-class fashions that match cleanly. These go away room for context, and nonetheless reply quick sufficient for coding, chat, and brokers. This information covers the fashions that really match, why every is price working, and the way 24GB will get spent.

How 24GB of VRAM truly will get spent

Three issues devour reminiscence throughout inference. Getting the break up proper decides whether or not a mannequin suits.

The first is mannequin weights. Their measurement relies upon on parameter depend and quantization. At Q4_K_M, a widespread home-inference default, every parameter prices roughly 0.58 bytes. A 32B mannequin due to this fact wants about 18–20GB in weights alone. Mixtral-style Mixture-of-Experts (MoE) fashions are the widespread lure right here. Every professional stays resident in VRAM even when solely a few route per token. So you measurement MoE reminiscence by whole parameters, by no means lively parameters.

The second is the KV cache, which grows with context size. Longer prompts and longer classes eat extra VRAM. The third is runtime overhead from the serving stack. A protected rule of thumb provides roughly 1–2GB for the KV cache and runtime at quick context.

Quantization is the lever that makes 24GB workable. Q4_K_M is the usual steadiness of high quality and footprint. Q5_K_M and Q6_K increase high quality at a reminiscence value. Q8_0 and BF16 are normally too giant for 30B-class fashions on a single 24GB card. The interactive instrument beneath permits you to swap quantization and see every mannequin’s match change reside.

The six finest native LLMs for a 24GB GPU

Every mannequin beneath carries a permissive license, both Apache 2.0 or MIT. All of them match a single 24GB card at Q4_K_M with room for context. They are grouped by the job each does finest.

Qwen3.6-27B — finest all-around and agentic coding

Alibaba’s Qwen3.6-27B is the strongest single default for the cardboard. It is a dense 27B mannequin launched in April 2026 beneath Apache 2.0. The launch focuses on agentic coding, repository-level reasoning, and frontend workflows. At Q4_K_M it wants roughly 16GB, leaving comfy headroom for context. Full mannequin playing cards and the changelog sit in the Qwen3.6 GitHub repository.

Qwen3.6-35B-A3B — quickest general-purpose possibility

The Qwen3.6-35B-A3B is a Mixture-of-Experts mannequin with 35B whole parameters and about 3B lively per token. It decodes far quicker than a dense 35B mannequin as a result of solely a fraction of weights hearth every step. The reminiscence footprint nonetheless tracks whole parameters, so plan for roughly 20GB at Q4_K_M. That makes it a tight however legitimate match, finest whenever you need pace for basic chat and power use.

Gemma 4 26B — multimodal and multilingual

Google DeepMind launched Gemma 4 on April 2, 2026, beneath Apache 2.0. It was the primary Gemma technology to ship with a absolutely open license, per the DeepMind Gemma page. The household spans edge fashions, a 12B unified multimodal mannequin, a 26B MoE with 3.8B lively, and a bigger dense flagship. The 26B MoE is the pure 24GB decide whenever you want imaginative and prescient enter and 140+ language protection.

Mistral Small 3.2 24B — polished every day assistant

Mistral’s Small line targets the on a regular basis assistant workload with low latency. Mistral Small 3.1 added multimodal enter and a longer context window, and Small 3.2 refined instruction following. It is a 24B dense mannequin beneath Apache 2.0, and the lightest footprint in this record at roughly 14GB at Q4_K_M. That headroom permits you to push context additional than the heavier 32B choices enable. In 2026 Mistral additionally shipped bigger successors, however these sit above the single-card tier.

gpt-oss-20b — the straightforward reasoning fallback

OpenAI’s gpt-oss-20b is an open-weight reasoning mannequin beneath Apache 2.0. It is a Mixture-of-Experts design with 21B whole parameters and three.6B lively per token. It ships in a native MXFP4 4-bit format, loading in roughly 14GB with beneficiant headroom. It is powerful at structured reasoning and power use, and weaker on broad world data.

DeepSeek-R1-Distill-Qwen-32B — deepest reasoning, tightest match

DeepSeek distilled its R1 reasoning traces into smaller dense fashions. The DeepSeek-R1-Distill-Qwen-32B is the 32B variant, constructed on a Qwen2.5 base and launched beneath an MIT license. At Q4_K_M it makes use of about 18–20GB, which is the tightest match in this information. It exposes its chain of thought by means of seen reasoning tokens, which is beneficial for sluggish, deliberate issues. Ready-made GGUF builds can be found from bartowski on Hugging Face.

What doesn’t match on 24GB

The frontier open fashions of 2026 are giant sparse MoE techniques. They are fairly good in efficiency and reasoning, however they don’t run on one client card. GLM-5.2 from Z.ai is a roughly 753B-total MoE. Moonshot’s Kimi K2.7 is round 1T whole. DeepSeek shipped V4 as a public preview in April 2026, with a V4-Pro checkpoint close to 1.6T parameters. Alibaba’s Qwen3.5-397B and Mistral Large 3 sit in the identical server-class vary.

Because MoE reminiscence tracks whole parameters, all of those want multi-GPU rigs or high-memory unified techniques. They are price figuring out as API choices. They don’t change what runs on a single 24GB card.

How to run these fashions regionally

Three runtimes cowl virtually each setup. Ollama is the best path, with computerized quantization choice and an OpenAI-compatible API. llama.cpp offers fantastic management over GGUF quantization and offload. vLLM is the throughput-focused server for heavier concurrent workloads.

Start with one mannequin that matches your most important job, not the most important file you’ll be able to load. Keep context beneath management, and let the cardboard do what it does finest. Run critical native AI with out sending a single token to a cloud endpoint.

Key Takeaways

• 24GB is the sensible ground: run right-sized 20B–35B fashions, not the most important 70B quant you’ll be able to squeeze in.
• Qwen3.6-27B is the strongest single default; DeepSeek-R1-Distill-Qwen-32B is the tightest match at ~18–20GB.
• MoE reminiscence tracks whole parameters, so each professional stays resident even when solely a few route per token.
• Quantization is the match lever: Q4_K_M is the house default; Q8_0 and BF16 hardly ever match a 30B mannequin on one card.
• GLM-5.2, Kimi K2.7, DeepSeek V4, and Mistral Large 3 are server-class and won’t run on a single 24GB GPU.

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