Best LLM in 24 GB of VRAM in 2026

Find the best LLM with 24 GB of VRAM depends primarily on the balance between context size, license, and intended use case. This configuration, typical of a RTX 3090 or RTX 4090, provides access to models in the 30- to 40-billion-parameter class with Q4 quantization, which radically changes the level of reasoning available locally compared with a 12 or 16 GB GPU. This article reviews the models that actually fit within this memory budget, their licenses, and the tradeoffs to understand before choosing.

Which models fit in 24 GB with Q4 quantization

With 24 GB of VRAM, the available headroom is between 17 and 22 GB of model weights once the KV cache is reserved. Several models sit just below or around this limit:

• Seed-OSS 36B Instruct : ~22 GB in Q4, Apache 2.0 license, exceptional 524,288-token context.
• Qwen 3.6 35B-A3B : ~21 GB in Q4, MoE architecture with 3B active parameters, 262,000-token context.
• Salamandra 40B Instruct : ~24 GB in Q4, at the upper limit of the envelope, Apache 2.0 license.
• Command R 35B v01 et Aya 23 35B : ~20 GB in Q4 each, under a CC-BY-NC 4.0 license (non-commercial use).
• Yi 1.5 34B Chat : ~20 GB in Q4, context limited to 4,096 tokens.

Le Mixtral 8x7B of Mistral AI, with 47B total parameters (MoE architecture), requires about 26 GB in Q4—slightly above 24 GB, which often requires more aggressive quantization (Q3) or partial CPU offloading to fit on a single card. The detailed specs by publisher remain available on Mistral AI on Hugging Face et BSC on Hugging Face for Salamandra.

RTX 3090 vs. RTX 4090: expected real-world throughput

Both cards share 24 GB of GDDR6X VRAM, but the RTX 4090 has higher memory bandwidth and compute power. On a 32–35B-class model in Q4:

• RTX 3090 : estimated throughput between 12 and 20 tokens/sec depending on the model and inference engine used.
• RTX 4090 : estimated throughput between 18 and 30 tokens/sec under the same conditions.

These figures vary significantly depending on the engine — llama.cpp (official GitHub) et Ollama (official GitHub) remain the simplest choices for quantized GGUF, while vLLM (documentation) is aimed more at high-throughput service with multiple simultaneous requests. On MoE models such as Qwen 3 30B-A3B or Nemotron Nano 3 30B-A3B, throughput is generally higher than that of a dense model of equivalent size because only a fraction of the parameters is activated per token.

Licenses: Apache 2.0, CC-BY-NC, and proprietary licenses

The license choice determines commercial use:

• Apache 2.0 (free commercial use): Mixtral 8x7B, Salamandra 40B, Seed-OSS 36B, Qwen 3.6 35B-A3B, nearly the entire Qwen 2.5/3 lineup, Gemma 4 31B, OLMo 3 32B, Granite 4.0 H-Small.
• CC-BY-NC 4.0 (noncommercial): Command R 35B v01, Aya 23 35B, Aya Expanse 32B, published by Cohere — see Cohere on Hugging Face.
• MIT : DeepSeek R1 Distill 32B et DeepSeek R2 32B, also highly permissive.
• Specific proprietary licenses : EXAONE 4.5 33B (EXAONE AI Model License, restricted use) and Nemotron 3 33B (NVIDIA Open Model License).

For a project intended for commercial production, favoring Apache 2.0 or MIT avoids legal gray areas related to non-commercial clauses.

Use case: reasoning, code, and versatility

At 24 GB of VRAM, three usage profiles stand out:

• Long reasoning : QwQ 32B and DeepSeek R1 Distill 32B target complex logical-chaining tasks, with scores generally reported on AIME and MMLU in the public benchmarks of the Open LLM Leaderboard (Hugging Face).
• Code : Qwen 2.5 Coder 32B remains a HumanEval reference for this size class; Qwen3-Coder 30B-A3B offers an extended context of 262,144 tokens for analyzing large repositories.
• Multilingual and general-purpose : Aya 23 35B and Aya Expanse 32B explicitly target multilingual coverage, while Jais 30B Chat v3 specializes in Arabic.

For a direct comparison between two frequent candidates in this range, the Aya Expanse 32B vs Qwen 2.5 32B et DeepSeek R1 32B vs QwQ 32B detail benchmark differences side by side.

Optimize the available context with the KV cache

A 32–35B model in Q4 already uses 17 to 22 GB of VRAM for its weights alone. The remaining headroom for the KV cache limits the context size that is actually usable, even if the specifications list a maximum context of 128,000 or 262,000 tokens. To understand how cache quantization (Q8, Q4) can free up several GB without changing models, see the guide to quantization and the KV cache and the guide to choosing Q4/Q5/Q8 quantization.

FAQ

Q: Which Apache 2.0 model should you choose for commercial use with 24 GB?

Seed-OSS 36B, Qwen 3.6 35B-A3B, and Qwen 2.5 32B Coder are three Apache 2.0 options that fit in Q4 within 24 GB. The choice depends on your needs: long context for Seed-OSS, code for Qwen 2.5 Coder, and MoE versatility for Qwen 3.6.

Q: Does Mixtral 8x7B really fit in 24 GB?

Its estimated Q4 footprint of ~26 GB slightly exceeds the 24 GB limit. Lower quantization (Q3) or a reduced context usually makes it fit, but with a loss of quality that must be checked case by case.

Q: RTX 3090 or RTX 4090 for these models?

Both offer the same 24 GB of VRAM, so they have the same model-loading capacity. The RTX 4090 delivers higher estimated tokens/sec throughput thanks to its higher memory bandwidth, which is useful for batch processing or long contexts.

Q: Can Command R 35B and Aya 23 35B be used in business environments?

Their CC-BY-NC 4.0 license restricts commercial use without a separate agreement with Cohere. For enterprise deployment, an Apache 2.0 or MIT model of equivalent size is preferable.

Q: Which tool should I use to run these models locally?

llama.cpp (official GitHub) et Ollama (official GitHub) cover quantized GGUF inference on a single card. For a self-hosted web interface, Open WebUI (official GitHub) is added on top of either one.

Q: Should I target 32 GB rather than 24 GB?

Moving to 32 GB enables longer contexts and more headroom for 35–40B models without compromising quantization. The 32 GB VRAM guide details the additional models accessible at this tier.

Conclusion

Le best LLM with 24 GB of VRAM is not unique: it depends on the license you need (Apache 2.0 for Seed-OSS 36B or Qwen 3.6 35B-A3B, CC-BY-NC for Command R and Aya), the use case (code, reasoning, multilingual) and the context actually required once the KV cache is taken into account. The configurator automatically cross-references these criteria, and the full catalog lists the 249 models with detailed VRAM specifications by quantization.