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 GTX 1650 4GB is the weakest GPU in this batch for local AI and should only be considered a last resort. With just 4 GB of VRAM, only the smallest models (1B–3B parameter range) can run without CPU offloading. It uses the Turing architecture (compute capability 7.5) and has basic Tensor Core support, but the 128 GB/s bandwidth and 4 GB limit make inference painfully slow. Only practical use case is as a test environment for model compatibility, not production inference.
Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model |
|---|---|---|
| LLM Chat (7B) | Won’t fit | Llama 3.1 8B Q4 |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 |
| LLM Large (70B) | Won’t fit | Llama 3.1 70B Q4 |
| Image Gen (SDXL) | Won't fit | SDXL 1.0 FP16 |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 |
| Video Short (25f) | Won't fit | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
Architecture
Turing is NVIDIA's first-generation RTX architecture, introducing dedicated RT and Tensor Cores to consumer GPUs for the first time. Built on TSMC's 12nm FinFET process.
AI Relevance
The first consumer architecture with Tensor Cores, enabling meaningful acceleration for INT8 and FP16 inference. However, limited VRAM (typically 6-11 GB) restricts modern LLM model sizes.
购买建议
有限制地可用于本地 AI
可运行 50 个顶级模型中的 2 个，主要是较小的模型。较大模型需要强量化或无法适配。
4.0 GB
VRAM
$149
建议零售价
$37/GB
每 GB VRAM 成本
最适合此 GPU 的模型
What will limit you first
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best upgrade itinerary
Unlocks 93 additional models that do not fit on the current setup.
想要更多余量？ RTX 2060 6GB (6.0 GB VRAM) 是下一步升级选择。
Chat
AQwen 3 1.7B 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
CStarCoder2 3B 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.
Agentic Coding
CStarCoder2 3B is a specialized fit for Agentic 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.
Reasoning
Cai21labs AI21 Jamba Reasoning 3B matches Reasoning 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.
RAG
CQwen2.5 3B Instruct is viable for RAG, 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.
BGE M3
Jina Embeddings v3
Qwen3-Coder 30B A3B InstructQwen 3.5 397B A17B
Devstral 2 123B Instruct
Kimi K2.5Kimi K2.6
DeepSeek V4 ProQwen 3.5 27BQwen 3.6 27BQwen 3.5 122B A10BQwen3-VL 30B A3B InstructQwen 3.6 35B A3BDeepSeek V4 FlashQwen 3.5 9BQwen 3.5 35B A3BMagistral Small 2507Devstral Small 2 24B InstructQwen 3 32BQwen 3 14BQwen 3 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72B
GPT-OSS 120BNemotron 3 Nano 30BQwen 3.5 4BQwen 3 8BQwen3-Coder-Next
Phi-4-reasoning-plus 14BDevstral Small 1.1
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2GPT-OSS 20BQwen 3 235B A22BQwen3-Coder 480B A35B InstructNemotron Cascade 2 30B A3BPhi-4 Mini Reasoning 4B
Gemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BNemotron Nano 8BMinistral 3 14B
EXAONE 4.0 32BGemma 4 26B A4B触手可及
高质量模型，只需稍多一点内存
Image & Video Generation
1 of 52 models can generate images or video on your GTX 1650 4GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~10.4s | D |
| Stable Diffusion 1.5Image | 512×768 | ~20.8s | F |
| Realistic Vision v5.1Image | 512×768 | ~20.8s | F |
| DreamShaper 8Image | 512×768 | ~20.8s | F |
| LCM DreamShaper v7Image | 512×768 | ~6.2s | F |
| PixArt-SigmaImage | 256×256 | ~1m 23s | F |
| FramePack I2VVideo | 256×256 | ~2m 33s/frame | F |
| SDXL TurboImage | 256×256 | ~10.4s | F |
| SDXL LightningImage | 256×256 | ~31.2s | F |
| Stable Diffusion XL 1.0Image | 256×256 | ~1m 23s | F |
| Playground v2.5Image | 256×256 | ~2m 5s | F |
| RealVisXL v5.0Image | 256×256 | ~1m 34s | F |
| DreamShaper XLImage | 256×256 | ~1m 34s | F |
| Juggernaut XL v9Image | 256×256 | ~1m 34s | F |
| Animagine XL 3.1Image | 256×256 | ~1m 34s | F |
| Pony Diffusion V6 XLImage | 256×256 | ~1m 34s | F |
| Animagine XL 4.0Image | 256×256 | ~1m 34s | F |
| Illustrious XLImage | 256×256 | ~1m 34s | F |
| Wan Video 2.1 1.3BVideo | 256×256 | ~1m 1s/frame | F |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~2m 25s | F |
| Flux.2 Klein 4BImage | 256×256 | ~24.9s | F |
| LTX Video 2BVideo | 256×256 | ~1m 12s/frame | F |
| KolorsImage | 256×256 | ~2m 46s | F |
| Stable CascadeImage | 256×256 | ~3m 28s | F |
| AuraFlow v0.3Image | 256×256 | ~6m 14s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~7m 37s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~1m 23s | F |
| CogVideoX 2BVideo | 256×256 | ~1m 12s/frame | F |
| HunyuanVideoVideo | 256×256 | ~2m 33s/frame | F |
| ChromaImage | 256×256 | ~1m 23s | F |
| Z-Image TurboImage | 256×256 | ~1m 26s | F |
| Flux.1 DevImage | 256×256 | ~6m 14s | F |
| Flux.1 SchnellImage | 256×256 | ~1m 13s | F |
| LTX Video 13BVideo | 256×256 | ~2m 33s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~6m 56s | F |
| AnimateDiff v1.5.3Video | 512×512 | ~37.9s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~1m 59s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~1m 44s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~1m 44s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~41.5s | F |
| Flux.1 Fill DevImage | 256×256 | ~5m 53s | F |
| Mochi 1 PreviewVideo | 256×256 | ~2m 17s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~2m 8s/frame | F |
| Helios 14BVideo | 256×256 | ~2m 37s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~2m 37s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~2m 37s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~2m 37s/frame | F |
| Qwen ImageImage | 256×256 | ~2m 20s | F |
| Qwen Image EditImage | 256×256 | ~2m 20s | F |
| Flux.2 DevImage | 256×256 | ~65m 31s | F |
| MAGI-1Video | 256×256 | ~3m 15s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~4m 6s | 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.
Upgrade paths
See what you unlock with more powerful hardware
升级选项
RTX 2060 6GB下一步升级Unlocks 93 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 39%.
~$349 MSRP
GTX 1060 6GBNVIDIA 升级Unlocks 93 additional models that do not fit on the current setup.
~$249 MSRP
Intel Arc B570 10GB最佳性价比Unlocks 164 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 74%.
~$219 MSRP
Unlocks 286 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 630%.
~$8,000 MSRP
GTX 1650 4GB (4 GB VRAM) can run these top models: BGE M3 (score: 82/100), Jina Embeddings v3 (score: 73/100), Qwen3-Coder 30B A3B Instruct (score: 0/100). See the full compatibility list above.
GTX 1650 4GB has 4 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, GTX 1650 4GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on GTX 1650 4GB, we recommend StarCoder2 3B. It achieves 40.5 tokens per second with 56K context window. StarCoder2 3B 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.
There are 4 upgrade path(s) from GTX 1650 4GB: RTX 2060 6GB, GTX 1060 6GB. Upgrading would unlock larger models and faster inference speeds.
Flux.1 Dev requires around 24 GB of usable memory at FP16. With 4 GB, GTX 1650 4GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
GTX 1650 4GB (4 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, Stable Diffusion 1.5 fits comfortably. For video, video generation is limited by available memory. Check the AI Capability Matrix above for detailed compatibility.
GTX 1650 4GB has limited capability for AI image generation with only 4 GB of usable memory. Stick to SD 1.5 at lower resolutions. For a better experience, consider hardware with at least 8 GB of usable accelerator memory.
Qwen 3.5 27B requires at least 16 GB of usable memory at Q4. With 4 GB, GTX 1650 4GB can run the 4B variant at Q4 (2.4 GB). Consider upgrading memory capacity for larger Qwen models.
With 4 GB on GTX 1650 4GB, stick to Q4_K_M for the best quality-to-size ratio. Only use Q2-Q3 if you must fit a model that otherwise would not load.
On GTX 1650 4GB, capacity is usually the first gate: if the model does not fit, bandwidth does not matter. But once a model fits, memory bandwidth is what largely determines tokens per second. In practice, you want enough memory to fit the model plus headroom, then as much bandwidth as your budget allows.
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