Best AI Models for NVIDIA RTX 4000 Ada Generation (20.0GB)
20 GB is a comfortable mid-range tier for local AI. Most 7B–13B models run smoothly at good quantization levels, and smaller models can run at near-full precision.
This memory tier strikes a nice balance between price and capability. Popular 7B models like Llama 3 8B, Mistral 7B, and Qwen 2.5 7B all run very well at Q4_K_M quantization with fast inference and reasonable context windows. You can also fit some larger 13B models at Q3–Q4, though you'll want to keep context lengths modest. Small models like Phi 3 Mini (3.8B) practically fly at Q8 or even FP16 quality.
Runs Well
- 7B models at Q4–Q6 quality with good speed
- Small models (3B–4B) at Q8 or FP16
- 9B models (Gemma 2 9B) at Q4_K_M
Challenging
- 13B–14B models need Q3 or lower
- 30B+ models do not fit in VRAM
- Long context (>8K tokens) with larger models
What LLMs Can NVIDIA RTX 4000 Ada Generation Run?
Showing compatibility for NVIDIA RTX 4000 Ada Generation
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 20.0 GB100% | — | 41K | TOO HEAVY | F0 | |
| Q4_K_M | 20.5 GB102% | — | 131K | TOO HEAVY | F0 | |
| Q4_K_M | 20.5 GB102% | — | 33K | TOO HEAVY | F0 |
NVIDIA RTX 4000 Ada Generation Specifications
- Brand
- NVIDIA
- Architecture
- Ada Lovelace
- VRAM
- 20.0 GB GDDR6
- Memory Bandwidth
- 360.0 GB/s
- CUDA Cores
- 6,144
- Tensor Cores
- 192
- FP16 Performance
- 106.90 TFLOPS
- TDP
- 130W
- Release Date
- 2023-08-09
- MSRP
- $1,250
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Frequently Asked Questions
- Can NVIDIA RTX 4000 Ada Generation run Llama 3 8B?
Yes, the NVIDIA RTX 4000 Ada Generation with 20 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 4000 Ada Generation good for AI?
The NVIDIA RTX 4000 Ada Generation has 20 GB of GDDR6, making it very good for running local LLM models. Most 7B-13B models run at good quality quantizations.
- How many parameters can NVIDIA RTX 4000 Ada Generation handle?
With 20 GB, the NVIDIA RTX 4000 Ada Generation 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 33B parameters.
- What quantization should I use on NVIDIA RTX 4000 Ada Generation?
For the best balance of quality and speed on 20 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 4000 Ada Generation for AI inference?
Speed depends on the model size and quantization. With 360.0 GB/s memory bandwidth, the NVIDIA RTX 4000 Ada Generation can typically achieve 25-45 tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.