Best AI Models for NVIDIA GeForce RTX 4060 (8.0GB)
8 GB is an entry-level tier for local AI. You can run small 7B models at lower quantization levels, which is great for experimenting but comes with quality and speed trade-offs.
With 8 GB, you're limited to smaller models and lower quantization levels, but it's still enough for a meaningful local AI experience. Phi 3 Mini (3.8B) and similar compact models run well at Q4_K_M. For 7B models like Mistral 7B and Llama 3 8B, you'll need Q2_K or Q3_K_M quantization, which reduces output quality. Think of this tier as ideal for learning and experimentation rather than production workloads.
Runs Well
- 3B–4B models at Q4–Q5 quality
- 7B models at Q2–Q3 (usable but reduced quality)
- Quick experiments and learning
Challenging
- 7B models at Q4+ (VRAM too tight)
- Any model above 7B parameters
- Long context windows even with small models
What LLMs Can NVIDIA GeForce RTX 4060 Run?
Showing compatibility for NVIDIA GeForce RTX 4060
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 5.5 GB69% | 32.0 t/s | 41K | GREAT FIT | S85 | |
| Q4_K_M | 5.3 GB66% | 33.5 t/s | 131K | GOOD FIT | A83 | |
| Q4_K_M | 6.1 GB76% | 29.0 t/s | 8K | GREAT FIT | S89 | |
| Q4_K_M | 5.0 GB62% | 35.4 t/s | 33K | GOOD FIT | A78 | |
| Q4_K_M | 5.4 GB67% | 32.9 t/s | 131K | GOOD FIT | A84 | |
| Q4_K_M | 5.4 GB67% | 32.8 t/s | 131K | GOOD FIT | A84 | |
| Q4_K_M | 4.9 GB62% | 35.9 t/s | 33K | GOOD FIT | A78 | |
| Q4_K_M | 5.0 GB62% | 35.4 t/s | 131K | GOOD FIT | A78 |
NVIDIA GeForce RTX 4060 Specifications
- Brand
- NVIDIA
- Architecture
- Ada Lovelace
- VRAM
- 8.0 GB GDDR6
- Memory Bandwidth
- 272.0 GB/s
- CUDA Cores
- 3,072
- Tensor Cores
- 96
- FP16 Performance
- 30.20 TFLOPS
- TDP
- 115W
- Release Date
- 2023-06-29
- MSRP
- $299
Get Started
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Frequently Asked Questions
- Can NVIDIA GeForce RTX 4060 run Llama 3 8B?
Yes, the NVIDIA GeForce RTX 4060 with 8 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 GeForce RTX 4060 good for AI?
The NVIDIA GeForce RTX 4060 has 8 GB of GDDR6, making it usable for running local LLM models. Small models run well, but larger 7B models need lower quantization.
- How many parameters can NVIDIA GeForce RTX 4060 handle?
With 8 GB, the NVIDIA GeForce RTX 4060 can handle models up to approximately 3-7B parameters depending on quantization. Using Q4_K_M quantization (the typical sweet spot), you can fit roughly 13B parameters.
- What quantization should I use on NVIDIA GeForce RTX 4060?
For the best balance of quality and speed on 8 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 GeForce RTX 4060 for AI inference?
Speed depends on the model size and quantization. With 272.0 GB/s memory bandwidth, the NVIDIA GeForce RTX 4060 can typically achieve 15-35 tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.