llmrun

Best LLMs for 24 GB VRAM

Enthusiast (RTX 3090, RTX 4090, RX 7900 XTX) — 30B+ models at Q4-Q6, 70B at aggressive quant

← 16 GB VRAM48 GB VRAM →

24 GB is the enthusiast tier for running AI models locally. It comfortably handles 7B–13B models at high quality and opens the door to larger 30B models at moderate quantization.

This is one of the most popular memory tiers for local AI, found in GPUs like the RTX 4090 and RTX 3090. You can run Llama 3 8B, Mistral 7B, and Qwen 2.5 7B at Q5_K_M or Q6_K quality with fast token generation and generous context windows. Larger 14B models like DeepSeek R1 Distill fit comfortably at Q4_K_M. For even bigger models, 30B class runs at Q2–Q3, but 70B models are generally too heavy for single-GPU inference at this tier.

Runs Well

  • 7B models (Llama 3 8B, Mistral 7B) at Q5–Q8 quality
  • 13B–14B models at Q4–Q5 quality
  • Small models (3B–4B) at FP16 precision
  • Multimodal models like LLaVA 7B

Challenging

  • 30B models only at Q2–Q3 quantization
  • 70B models do not fit in VRAM
  • Large context windows with 14B+ models

GPUs with ~24.0 GB VRAM

All 12 GPUs

NVIDIA L4

NVIDIA · Ada Lovelace

24 GB
300.0 GB/s7,424 CUDA72W TDP

NVIDIA GeForce RTX 3090

NVIDIA · Ampere

24 GB
936.2 GB/s10,496 CUDA350W TDP$1,499

AMD Radeon RX 7900 XTX

AMD · RDNA 3

24 GB
960.0 GB/s6,144 SP355W TDP$999

NVIDIA TITAN RTX

NVIDIA · Turing

24 GB
672.0 GB/s4,608 CUDA280W TDP$2,499

NVIDIA RTX PRO 4000 Blackwell

NVIDIA · Blackwell

24 GB
672.0 GB/s8,960 CUDA145W TDP$1,500

NVIDIA GeForce RTX 5090 Laptop GPU

NVIDIA · Blackwell

24 GB
896.0 GB/s10,496 CUDA150W TDP

Models That Fit in 24 GB VRAM

Speed estimated for NVIDIA GeForce RTX 4090

105 models · 7 excellent · 15 good

LLM models ranked by compatibility and performance
ModelVRAMGrade
Qwen2.5 Coder 32B Instruct32.8B
Q4_K_M·32.0 t/s tok/s·33K ctx·GOOD FIT
20.5 GBA70
DeepSeek R1 Distill Qwen 32B32.8B
Q4_K_M·32.0 t/s tok/s·131K ctx·GOOD FIT
20.5 GBA70
QwQ 32B32.8B
Q4_K_M·32.0 t/s tok/s·41K ctx·GOOD FIT
20.5 GBA70
Phi 414.7B
Q4_K_M·68.8 t/s tok/s·16K ctx·FAIR FIT
9.5 GBB55
Qwen1.5 32B Chat32.5B
Q4_K_M·32.2 t/s tok/s·33K ctx·GOOD FIT
20.3 GBA70
Qwen1.5 32B32.5B
Q4_K_M·32.2 t/s tok/s·33K ctx·GOOD FIT
20.3 GBA70
Gemma 4 31B IT32.7B
Q4_K_M·30.9 t/s tok/s·262K ctx·FAIR FIT
21.2 GBB60
Qwen1.5 14B14.2B
Q4_K_M·62.5 t/s tok/s·33K ctx·FAIR FIT
10.5 GBB59
Qwen2.5 Coder 32B32.8B
Q4_K_M·32.0 t/s tok/s·33K ctx·GOOD FIT
20.5 GBA70
Gemma 3 12B IT12.2B
Q4_K_M·81.5 t/s tok/s·33K ctx·FAIR FIT
8.0 GBB49
Gemma 4 12B IT12.0B
Q4_K_M·79.6 t/s tok/s·262K ctx·FAIR FIT
8.2 GBB49
Phi 4 Reasoning14.7B
Q4_K_M·68.8 t/s tok/s·33K ctx·FAIR FIT
9.5 GBB55
Mistral Nemo Instruct 240712.2B
Q4_K_M·81.2 t/s tok/s·131K ctx·FAIR FIT
8.1 GBB49
Llama 2 13B Chat HF13.0B
Q4_K_M·76.3 t/s tok/s·FAIR FIT
8.6 GBB51
Llama 2 13B HF13.0B
Q4_K_M·76.3 t/s tok/s·FAIR FIT
8.6 GBB51
Deepseek Coder 33B Instruct33.3B
Q4_K_M·31.5 t/s tok/s·16K ctx·FAIR FIT
20.8 GBB63

Frequently Asked Questions

What models can I run with 24.0 GB VRAM?

With 24.0 GB VRAM, you can run 1267 LLM models at various quantization levels. Popular models that fit well include Qwen3.6 27B, Gemma 3 27B IT, Gemma 4 26B A4B IT. 69 models achieve excellent performance at this VRAM level. At this tier, you have the flexibility to choose higher quantizations (Q5/Q6) for better quality on smaller models, or run larger models at Q4.

Is 24.0 GB enough for local AI?

24.0 GB is excellent for local AI. You have access to 1267 compatible models, from small 7B assistants to large 30B+ parameter models. This is the enthusiast tier where most popular open-source LLMs work well out of the box. You can run coding assistants, chat models, and reasoning models without worrying about VRAM limits.

What GPU should I get for 24.0 GB VRAM?

Popular GPUs with ~24.0 GB include NVIDIA L4, NVIDIA GeForce RTX 3090, AMD Radeon RX 7900 XTX. The NVIDIA GeForce RTX 4090 leads in memory bandwidth at 1008.0 GB/s, which translates directly to faster token generation. When choosing a GPU for AI, memory bandwidth matters as much as VRAM capacity — it determines how fast the model can generate text. A newer GPU with the same VRAM but higher bandwidth will produce tokens significantly faster.

Higher memory bandwidth = faster token generation. All these GPUs have approximately 24 GB VRAM, but speed varies significantly by bandwidth.

Memory bandwidth comparison

1008 GB/s
1008 GB/s
960 GB/s
936.2 GB/s
896 GB/s
How to choose the right model size for 24.0 GB?

The key rule: your model must fit in VRAM including KV cache overhead. With 24.0 GB, here's a practical guide: 7B models at Q6–Q8 give you the best quality output. 14B models at Q4–Q5 offer a great quality/size balance. 30B+ models fit at Q4 but leave less room for context. Start with a 7B model at high quality and scale up as needed.

Should I get 24.0 GB or 48.0 GB for AI?

Upgrading from 24.0 GB to 48.0 GB gives you significantly more flexibility. At 24.0 GB you can run 1267 models; with 48.0 GB you'll unlock larger models and higher-quality quantizations. If budget allows, the extra VRAM is always worth it for AI workloads — you can't add VRAM later.