Best LLMs for 24 GB VRAM
Enthusiast (RTX 3090, RTX 4090, RX 7900 XTX) — 30B+ models at Q4-Q6, 70B at aggressive quant
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
NVIDIA L4
NVIDIA · Ada Lovelace
NVIDIA GeForce RTX 4090
NVIDIA · Ada Lovelace
NVIDIA GeForce RTX 3090 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 3090
NVIDIA · Ampere
AMD Radeon RX 7900 XTX
AMD · RDNA 3
NVIDIA RTX A5000
NVIDIA · Ampere
Models That Fit in 24 GB VRAM
Speed estimated for NVIDIA GeForce RTX 4090
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 5.4 GB22% | 122.0 t/s | 131K | EASY RUN | C37 | |
| Q4_K_M | 5.4 GB22% | 121.6 t/s | 131K | EASY RUN | C37 | |
| Q4_K_M | 5.0 GB21% | 131.3 t/s | 131K | EASY RUN | C36 | |
| Q8_0 | 4.9 GB20% | 133.4 t/s | 4K | EASY RUN | C35 | |
| Q4_K_M | 2.9 GB12% | 226.7 t/s | 41K | EASY RUN | C31 | |
| Q4_K_M | 2.6 GB11% | 248.2 t/s | 2K | EASY RUN | C31 | |
| Q4_K_M | 2.0 GB8% | 330.9 t/s | 131K | EASY RUN | D29 | |
| Q4_K_M | 2.9 GB12% | 229.9 t/s | 131K | EASY RUN | C31 |
Frequently Asked Questions
- What models can I run with 24.0 GB VRAM?
With 24.0 GB VRAM, you can run most 7B-30B models at good quality, and 70B models at lower quantizations.
- Is 24.0 GB enough for local AI?
24.0 GB is excellent for local AI. You can comfortably run a wide range of models from small 7B assistants to large 30B models. This is the enthusiast tier where most popular models work well.
- What GPU should I get for 24.0 GB VRAM?
There are several GPUs with approximately 24.0 GB VRAM at different price points. Popular choices include NVIDIA L4, NVIDIA GeForce RTX 4090, NVIDIA GeForce RTX 3090 Ti. Memory bandwidth also matters — higher bandwidth means faster token generation. Check the GPU cards above for specific specs and pricing.
- What quantization works best with 24.0 GB?
For 24.0 GB, Q4_K_M is typically the best starting quantization — it offers a good balance of model quality and VRAM usage. You can also try Q5_K_M or Q6_K for better quality with 7B models. Use Q2_K or Q3_K_M only when you need to squeeze in a model that's otherwise too large.