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NVIDIA
Operating mode
Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
The RTX 3050 8GB is a budget Ampere card that offers just enough VRAM to run 7B models at FP16 — but barely. The 8 GB VRAM fits a 7B model in Q4 with some room for KV cache, while the 3rd-gen Tensor Cores with INT8 sparsity acceleration give it a meaningful edge over Turing-era cards. Memory bandwidth at 224 GB/s is its main weakness — token generation on a loaded 7B model will feel sluggish compared to even the RTX 3060 Ti. Good for first-time AI experimentation on a tight budget.
Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
Architecture
Ampere is NVIDIA's second-generation RTX architecture, built on Samsung's 8nm process. It introduced 3rd-generation Tensor Cores with support for sparsity-accelerated INT8 operations and improved FP16 throughput over Turing.
AI Relevance
Sparsity-aware Tensor Cores can effectively double throughput for structured sparse workloads. However, the lack of FP8 support means quantized inference is less efficient than Ada Lovelace or Blackwell.
Chat
SQwen 3.5 4B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Coding
ACodestral Mamba 7B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Agentic Coding
AJust out of reach
High-quality models that need a bit more memory
Image & Video Generation
21 of 52 models can generate images or video on your RTX 3050 8GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~2.6s | S |
| Stable Diffusion 1.5Image | 512×768 | ~5.2s | S |
| Realistic Vision v5.1Image | 512×768 | ~5.2s | S |
| DreamShaper 8Image | 512×768 | ~5.2s | S |
| LCM DreamShaper v7 |
Upgrade paths
See what you unlock with more powerful hardware
Upgrade options
Unlocks 33 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 136%.
~$699 MSRP
Unlocks 34 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 136%.
~$999 MSRP
Unlocks 74 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 13%.
~$329 MSRP
Unlocks 155 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 487%.
~$8,000 MSRP
Compare with similar
| Won’t fit |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 | ~~55.7s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~1m 34s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~1m 55s per image |
| Video Short (25f) | Won't fit | LTX Video 2B | ~~18.2s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~53.6s/frame |
Gemma 4 E2B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Reasoning
ACodestral Mamba 7B is viable for Reasoning, but is not the most specialized choice. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
RAG
AGranite 4.1 3B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
| 512×768 |
| ~1.6s |
| S |
| PixArt-SigmaImage | 256×256 | ~21s | S |
| FramePack I2VVideo | 256×256 | ~38.5s/frame | A |
| SDXL TurboImage | 256×256 | ~7s | A |
| SDXL LightningImage | 256×256 | ~20.9s | B |
| Stable Diffusion XL 1.0Image | 256×256 | ~55.7s | B |
| Playground v2.5Image | 256×256 | ~31.5s | B |
| RealVisXL v5.0Image | 256×256 | ~1m 3s | B |
| DreamShaper XLImage | 256×256 | ~1m 3s | B |
| Juggernaut XL v9Image | 256×256 | ~1m 3s | B |
| Animagine XL 3.1Image | 256×256 | ~1m 3s | B |
| Pony Diffusion V6 XLImage | 256×256 | ~1m 3s | B |
| Animagine XL 4.0Image | 256×256 | ~1m 3s | B |
| Illustrious XLImage | 256×256 | ~1m 3s | B |
| Wan Video 2.1 1.3BVideo | 256×256 | ~15.3s/frame | D |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~36.7s | D |
| Flux.2 Klein 4BImage | 256×256 | ~6.3s | D |
| LTX Video 2BVideo | 256×256 | ~18.2s/frame | F |
| KolorsImage | 256×256 | ~42s | F |
| Stable CascadeImage | 256×256 | ~52.5s | F |
| AuraFlow v0.3Image | 256×256 | ~1m 34s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~1m 55s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~21s | F |
| CogVideoX 2BVideo | 256×256 | ~18.2s/frame | F |
| HunyuanVideoVideo | 256×256 | ~38.5s/frame | F |
| ChromaImage | 256×256 | ~21s | F |
| Z-Image TurboImage | 256×256 | ~21.7s | F |
| Flux.1 DevImage | 256×256 | ~1m 34s | F |
| Flux.1 SchnellImage | 256×256 | ~18.4s | F |
| LTX Video 13BVideo | 256×256 | ~38.5s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~1m 45s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~9.6s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~30.1s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~26.3s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~26.3s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~10.5s | F |
| Flux.1 Fill DevImage | 256×256 | ~1m 29s | F |
| Mochi 1 PreviewVideo | 256×256 | ~34.7s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~32.2s/frame | F |
| Helios 14BVideo | 256×256 | ~39.7s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~39.7s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~39.7s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~39.7s/frame | F |
| Qwen ImageImage | 256×256 | ~35.3s | F |
| Qwen Image EditImage | 256×256 | ~35.3s | F |
| Flux.2 DevImage | 256×256 | ~16m 33s | F |
| MAGI-1Video | 256×256 | ~49.2s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~1m 2s | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
There are 4 upgrade path(s) from RTX 3050 8GB: RTX 3080 10GB, RTX 2080 Ti 11GB. Upgrading would unlock larger models and faster inference speeds.
Buying advice
Usable for local AI with limits
Can run 7 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
8.0 GB
VRAM
$249
MSRP
$31/GB
Cost per GB VRAM
Best models for this GPU
What will limit you first
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best upgrade itinerary
Unlocks 33 additional models that do not fit on the current setup.
Want more headroom? RTX 3080 10GB (10.0 GB VRAM) is the next step up.
