Best AI Models for NVIDIA GeForce RTX 3050 8GB (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 3050 8GB Run?
66 models · 6 excellent · 19 good
Showing compatibility for NVIDIA GeForce RTX 3050 8GB
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·26.4 t/s tok/s·41K ctx·GREAT FIT | Q4_K_M | 5.5 GB | 26.4 t/s | 41K | GREAT FIT | S85 |
Q4_K_M·27.4 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.3 GB | 27.4 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·27.5 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.3 GB | 27.5 t/s | 131K | GOOD FIT | A83 |
Q4_K_M·23.9 t/s tok/s·8K ctx·GREAT FIT | Q4_K_M | 6.1 GB | 23.9 t/s | 8K | GREAT FIT | S89 |
Q4_K_M·24.2 t/s tok/s·33K ctx·GREAT FIT | Q4_K_M | 6.0 GB | 24.2 t/s | 33K | GREAT FIT | S90 |
Q4_K_M·25.3 t/s tok/s·66K ctx·GREAT FIT | Q4_K_M | 5.8 GB | 25.3 t/s | 66K | GREAT FIT | S88 |
Q4_K_M·26.4 t/s tok/s·131K ctx·GREAT FIT | Q4_K_M | 5.5 GB | 26.4 t/s | 131K | GREAT FIT | S85 |
Q4_K_M·29.2 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 5.0 GB | 29.2 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·27.0 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 27.0 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·27.0 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 27.0 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·29.6 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 4.9 GB | 29.6 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·26.9 t/s tok/s·16K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 26.9 t/s | 16K | GOOD FIT | A84 |
Q4_K_M·25.1 t/s tok/s·4K ctx·GREAT FIT | Q4_K_M | 5.8 GB | 25.1 t/s | 4K | GREAT FIT | S88 |
Q4_K_M·29.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.0 GB | 29.2 t/s | 131K | GOOD FIT | A78 |
Q4_K_M·28.1 t/s tok/s·GOOD FIT | Q4_K_M | 5.2 GB | 28.1 t/s | — | GOOD FIT | A82 |
Q4_K_M·28.5 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 5.1 GB | 28.5 t/s | 33K | GOOD FIT | A81 |
NVIDIA GeForce RTX 3050 8GB Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- Compute Capability
- 8.6 (CUDA SM version)
- VRAM
- 8.0 GB GDDR6
- Memory Bandwidth
- 224.0 GB/s
- CUDA Cores
- 2,560
- Stream Processors
- 2,560
- Tensor Cores
- 80
- FP16 Performance
- 18.20 TFLOPS
- TDP
- 130W
- Release Date
- 2022-01-27
- MSRP
- $249
Get Started
GPUs to Consider Over NVIDIA GeForce RTX 3050 8GB
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5080
NVIDIA · Blackwell
NVIDIA GeForce RTX 3080 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 5070 Ti
NVIDIA · Blackwell
NVIDIA GeForce RTX 3080
NVIDIA · Ampere
NVIDIA GeForce RTX 4080 SUPER
NVIDIA · Ada Lovelace
NVIDIA GeForce RTX 4080
NVIDIA · Ada Lovelace
Frequently Asked Questions
- Can NVIDIA GeForce RTX 3050 8GB run Qwen3 8B?
Yes, the NVIDIA GeForce RTX 3050 8GB with 8 GB can run Qwen3 8B, Gemma 4 E4B IT, Llama 3.1 8B Instruct, and 964 other models. 88 models run at excellent quality, and 302 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA GeForce RTX 3050 8GB good for AI?
The NVIDIA GeForce RTX 3050 8GB has 8 GB of GDDR6, making it usable for running local AI models. It supports 390 models at good quality or better. With 224.0 GB/s memory bandwidth, it delivers reasonable token generation speeds. You can run smaller models and experiment with quantized 7B models.
- How many parameters can NVIDIA GeForce RTX 3050 8GB handle?
With 8 GB, the NVIDIA GeForce RTX 3050 8GB supports models from 1B to 7B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 13B parameters. Smaller 3B–7B models fit at Q3–Q4 quantization.
- What quantization should I use on NVIDIA GeForce RTX 3050 8GB?
For the best balance of quality and speed on the NVIDIA GeForce RTX 3050 8GB, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. If a model barely fits, drop to Q3_K_M — quality loss is noticeable but still useful for chat. Avoid Q2_K unless you just want to test whether a model works at all.
- How fast is NVIDIA GeForce RTX 3050 8GB for AI inference?
With 224.0 GB/s memory bandwidth, the NVIDIA GeForce RTX 3050 8GB achieves approximately 32 tokens/sec on a 7B model at Q4_K_M — that's comfortable for real-time interactive chat. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (224 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA GeForce RTX 3050 8GB
~26 tok/s~27 tok/s~28 tok/s~24 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA GeForce RTX 3050 8GB?
The top-rated models for the NVIDIA GeForce RTX 3050 8GB are Qwen3 8B, Gemma 4 E4B IT, Llama 3.1 8B Instruct. 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.
- What power supply and cooling does NVIDIA GeForce RTX 3050 8GB need?
The NVIDIA GeForce RTX 3050 8GB has a TDP of 130 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 550 W PSU or larger. It's a relatively low-power card, so most mid-tower cases with basic airflow handle it comfortably. Still, ensure the GPU slot has clearance and vents aren't obstructed during long inference sessions.