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?
27 models · 1 excellent · 1 good
Showing compatibility for NVIDIA RTX 4000 Ada Generation
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·13.0 t/s tok/s·8K ctx·FAIR FIT | Q4_K_M | 18.0 GB | 13.0 t/s | 8K | FAIR FIT | B52 |
Q4_K_M·82.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 82.1 t/s | 131K | EASY RUN | C32 |
Q4_K_M·231.7 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 231.7 t/s | 2K | EASY RUN | D28 |
Q4_K_M·354.5 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.7 GB | 354.5 t/s | 131K | EASY RUN | D27 |
Q4_K_M·354.5 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 0.7 GB | 354.5 t/s | 33K | EASY RUN | D27 |
Q4_K_M·12.9 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 18.1 GB | 12.9 t/s | 131K | FAIR FIT | B48 |
Q4_K_M·177.3 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.3 GB | 177.3 t/s | 8K | EASY RUN | D29 |
Q4_K_M·11.8 t/s tok/s·41K ctx·TOO HEAVY | Q4_K_M | 19.8 GB | 11.8 t/s | 41K | TOO HEAVY | D15 |
Q4_K_M·— tok/s·41K ctx·TOO HEAVY | Q4_K_M | 20.0 GB | — | 41K | TOO HEAVY | F0 |
Q4_K_M·— tok/s·131K ctx·TOO HEAVY | Q4_K_M | 20.5 GB | — | 131K | TOO HEAVY | F0 |
Q4_K_M·— tok/s·33K ctx·TOO HEAVY | Q4_K_M | 20.5 GB | — | 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
Get Started
GPUs to Consider Over NVIDIA RTX 4000 Ada Generation
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5090
NVIDIA · Blackwell
NVIDIA GeForce RTX 3090 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 4090
NVIDIA · Ada Lovelace
AMD Radeon RX 7900 XTX
AMD · RDNA 3
NVIDIA GeForce RTX 3090
NVIDIA · Ampere
AMD Radeon RX 7900 XT
AMD · RDNA 3
Frequently Asked Questions
- Can NVIDIA RTX 4000 Ada Generation run GPT OSS 20B?
Yes, the NVIDIA RTX 4000 Ada Generation with 20 GB can run GPT OSS 20B, Mistral Small 24B Instruct 2501, Phi 4, and 1069 other models. 102 models run at excellent quality, and 112 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- 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 AI models. It supports 214 models at good quality or better. With 360.0 GB/s memory bandwidth, it delivers reasonable token generation speeds. This is a solid mid-range card for running 7B–14B parameter models at good quality.
- How many parameters can NVIDIA RTX 4000 Ada Generation handle?
With 20 GB, the NVIDIA RTX 4000 Ada Generation supports models from 3B to 14B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 33B parameters. 7B models run at high quality (Q5/Q6), while 14B models fit comfortably at Q4.
- What quantization should I use on NVIDIA RTX 4000 Ada Generation?
For the best balance of quality and speed on the NVIDIA RTX 4000 Ada Generation, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. You can step up to Q5_K_M for 7B models to get better quality. For 14B models that just barely fit, Q4_K_M is ideal.
- How fast is NVIDIA RTX 4000 Ada Generation for AI inference?
With 360.0 GB/s memory bandwidth, the NVIDIA RTX 4000 Ada Generation achieves approximately 52 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~26 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (360 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA RTX 4000 Ada Generation
~18 tok/s~16 tok/s~26 tok/s~30 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA RTX 4000 Ada Generation?
The top-rated models for the NVIDIA RTX 4000 Ada Generation are GPT OSS 20B, Mistral Small 24B Instruct 2501, Phi 4. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.