Best AI Models for NVIDIA RTX A5000 (24.0GB)
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
What LLMs Can NVIDIA RTX A5000 Run?
Showing compatibility for NVIDIA RTX A5000
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 5.4 GB22% | 92.6 t/s | 131K | EASY RUN | C37 | |
| Q4_K_M | 5.0 GB21% | 100.0 t/s | 131K | EASY RUN | C36 | |
| Q4_K_M | 21.4 GB89% | 23.3 t/s | 4K | FAIR FIT | B56 | |
| Q8_0 | 4.9 GB20% | 101.7 t/s | 4K | EASY RUN | C35 | |
| Q4_K_M | 2.9 GB12% | 172.7 t/s | 41K | EASY RUN | C31 | |
| Q4_K_M | 2.6 GB11% | 189.1 t/s | 2K | EASY RUN | C31 | |
| Q4_K_M | 2.0 GB8% | 252.1 t/s | 131K | EASY RUN | D29 | |
| Q4_K_M | 2.9 GB12% | 175.2 t/s | 131K | EASY RUN | C31 |
NVIDIA RTX A5000 Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- VRAM
- 24.0 GB GDDR6
- Memory Bandwidth
- 768.0 GB/s
- CUDA Cores
- 8,192
- Tensor Cores
- 256
- FP16 Performance
- 111.10 TFLOPS
- TDP
- 230W
- Release Date
- 2021-04-12
- MSRP
- $2,250
Get Started
Similar GPUs for Running AI Models
AMD Radeon RX 7900 XTX
AMD · RDNA 3
NVIDIA GeForce RTX 3090
NVIDIA · Ampere
NVIDIA GeForce RTX 3090 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 4090
NVIDIA · Ada Lovelace
NVIDIA L4
NVIDIA · Ada Lovelace
Frequently Asked Questions
- Can NVIDIA RTX A5000 run Llama 3 8B?
Yes, the NVIDIA RTX A5000 with 24 GB can run Llama 3 8B at Q4_K_M quantization with good performance. At this VRAM level, you can expect smooth token generation and responsive inference for chat and coding tasks.
- Is NVIDIA RTX A5000 good for AI?
The NVIDIA RTX A5000 has 24 GB of GDDR6, making it excellent for running local LLM models. You can run most popular 7B-30B models at good quality.
- How many parameters can NVIDIA RTX A5000 handle?
With 24 GB, the NVIDIA RTX A5000 can handle models up to approximately 30-70B parameters depending on quantization. Using Q4_K_M quantization (the typical sweet spot), you can fit roughly 40B parameters.
- What quantization should I use on NVIDIA RTX A5000?
For the best balance of quality and speed on 24 GB, Q4_K_M is the recommended starting point. If you have headroom, try Q5_K_M for better quality. For larger models that barely fit, Q3_K_M or Q2_K can squeeze them in at the cost of some output quality.
- How fast is NVIDIA RTX A5000 for AI inference?
Speed depends on the model size and quantization. With 768.0 GB/s memory bandwidth, the NVIDIA RTX A5000 can typically achieve 30-50+ tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.