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 3090 24GB is the gold standard for consumer Ampere AI inference. Its 24 GB of GDDR6X VRAM is large enough to run 13B models at FP16 and 30B models at Q4 quantization — territory most consumer cards can't reach. With 936 GB/s bandwidth, decode throughput is strong. The 350W TDP is significant, but for serious local AI work the 3090 was the go-to consumer GPU before the RTX 4090. Still excellent on the used market if you need maximum VRAM without spending RTX 4090 prices.
Beyond LLMs
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model |
|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 |
| LLM Large (70B) | Won’t fit | Llama 3.1 70B Q4 |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 |
| Image Gen (Flux) | Runs with offload | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 |
| Video Short (25f) | Runs natively | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
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.
Consejo de compra
Excelente opción para IA local
Ejecuta 26 de 50 modelos principales bien — un todoterreno sólido para inferencia local.
24.0 GB
VRAM
$1,499
PVP
$62/GB
Coste por GB de VRAM
Mejores modelos para esta GPU
What will limit you first
Este setup está bastante equilibrado para este modelo.
Hay muy poco margen de memoria
Puedes ejecutar el modelo, pero queda poco margen para más contexto, batches mayores, otras apps o futuras revisiones del modelo.
Best upgrade itinerary
Compra margen, no solo el mínimo para que quepa
Un escalón algo mayor de memoria te da más seguridad para crecer en contexto y hace la recomendación más resistente a futuro.
Desbloquea 1 modelos adicionales que hoy no caben en tu setup.
¿Quieres más margen? MacBook Pro M4 Max 36GB (36.0 GB unified memory) es el siguiente paso.
Cost vs cloud API
Assumes 4 hours/day of active inference at 103 tok/s, RTX 3090 24GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
44.3M
Tokens/month at this pace
$28.5
Monthly local cost
$443
Same tokens on cloud API
$0.644
Local $/1M tokens
Break-even: pays for itself in 1.8 months vs cloud API at this workload. Price reference: $800 (used market).
Chat
SThis model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
Coding
SThis model is a direct match for coding. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.
Agentic Coding
SThis model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, lm-studio.
Reasoning
SThis model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
RAG
AThis model is a direct match for rag. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.
Qwen3-VL 30B A3B InstructQwen3-Coder 30B A3B Instruct
GPT-OSS 20BQwen 3 14B
Phi-4-reasoning-plus 14BQwen 3 30B A3BQwen 3.5 9BQwen 3.5 27B
Magistral Small 2507Devstral Small 2 24B InstructQwen 3.6 27BQwen 3 8BNemotron Cascade 2 30B A3BDevstral Small 1.1Ministral 3 14B
Gemma 4 26B A4BNemotron 3 Nano 30BQwen 3.5 4BNemotron Nano 8BQwen 3.5 35B A3BPhi-4 Mini Reasoning 4BQwen 3 32BQwen 3.6 35B A3B
Jina Embeddings v3
BGE M3
EXAONE 4.0 32BQwen 3.5 397B A17BDevstral 2 123B Instruct
Kimi K2.5Kimi K2.6
DeepSeek V4 ProQwen 3.5 122B A10BDeepSeek V4 FlashMistral Small 4 119B
Command A 111BQwen 2.5 VL 72BGPT-OSS 120BQwen3-Coder-Next
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2Qwen 3 235B A22BQwen3-Coder 480B A35B InstructGemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BCasi al alcance
Modelos de alta calidad que necesitan un poco más de memoria
Image & Video Generation
41 of 52 models can generate images or video on your RTX 3090 24GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 600ms | S |
| Stable Diffusion 1.5Image | 512×768 | ~1.3s | S |
| Realistic Vision v5.1Image | 512×768 | ~1.3s | S |
| DreamShaper 8Image | 512×768 | ~1.3s | S |
| LCM DreamShaper v7Image | 512×768 | 400ms | S |
| PixArt-SigmaImage | 1024×1024 | ~5s | S |
| FramePack I2VVideo | 256×256 | ~9.2s/frame | S |
| SDXL TurboImage | 512×512 | 600ms | S |
| SDXL LightningImage | 1024×1024 | ~1.9s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~5s | S |
| Playground v2.5Image | 1024×1024 | ~7.5s | S |
| RealVisXL v5.0Image | 1024×1024 | ~5.6s | S |
| DreamShaper XLImage | 1024×1024 | ~5.6s | S |
| Juggernaut XL v9Image | 1024×1024 | ~5.6s | S |
| Animagine XL 3.1Image | 1024×1024 | ~5.6s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~5.6s | S |
| Animagine XL 4.0Image | 1024×1024 | ~5.6s | S |
| Illustrious XLImage | 1024×1024 | ~5.6s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~3.7s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~8.8s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~1.5s | S |
| LTX Video 2BVideo | 768×512 | ~4.4s/frame | S |
| KolorsImage | 1024×1024 | ~10s | S |
| Stable CascadeImage | 1024×1024 | ~12.5s | S |
| AuraFlow v0.3Image | 1536×1536 | ~22.6s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~27.6s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~5s | S |
| CogVideoX 2BVideo | 720×480 | ~4.4s/frame | A |
| HunyuanVideoVideo | 256×256 | ~9.2s/frame | A |
| ChromaImage | 256×256 | ~9.2s | A |
| Z-Image TurboImage | 1536×1536 | ~5.2s | B |
| Flux.1 DevImage | 256×256 | ~22.6s | B |
| Flux.1 SchnellImage | 256×256 | ~4.4s | B |
| LTX Video 13BVideo | 256×256 | ~9.2s/frame | B |
| Flux.1 Kontext DevImage | 256×256 | ~25.1s | B |
| AnimateDiff v1.5.3Video | 512×768 | ~2.3s/frame | B |
| Cosmos Diffusion 7BVideo | 256×256 | ~13.9s/frame | B |
| CogVideoX 5BVideo | 256×256 | ~13.2s/frame | B |
| Wan2.2 TI2V 5BVideo | 256×256 | ~13.2s/frame | B |
| Flux.2 Klein 9BImage | 256×256 | ~4.6s | D |
| Flux.1 Fill DevImage | 256×256 | ~21.3s | D |
| Mochi 1 PreviewVideo | 256×256 | ~8.3s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~7.7s/frame | F |
| Helios 14BVideo | 256×256 | ~9.5s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~9.5s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~9.5s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~9.5s/frame | F |
| Qwen ImageImage | 256×256 | ~8.4s | F |
| Qwen Image EditImage | 256×256 | ~8.4s | F |
| Flux.2 DevImage | 256×256 | ~3m 57s | F |
| MAGI-1Video | 256×256 | ~11.8s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~14.9s | 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.
Multi-GPU scaling
Scale out with multiple GPUs for larger models. PCIe interconnect with 30% scaling overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× RTX | 24 GB | 319/374 | 936 GB/s |
| 2× RTX | 48 GB | 338/374 | 1,310 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.7× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Opciones de mejora
2× RTX 3090 24GBMulti-GPUDesbloquea 19 modelos adicionales que hoy no caben en tu setup.
El scale-out solo compensa si la plataforma host tiene suficientes PCIe lanes, separación entre slots, potencia y refrigeración.
Cuanto más grande es el setup, más importa el runtime. En multi-GPU y serving para varios usuarios es donde vLLM, SGLang, TGI, TensorRT-LLM o un llama.cpp afinado empiezan a justificar su complejidad.
~$1,499 MSRP
Desbloquea 1 modelos adicionales que hoy no caben en tu setup.
~$2,499 MSRP
RTX 5000 Ada 32GBMejora NVIDIADesbloquea 6 modelos adicionales que hoy no caben en tu setup.
~$4,000 MSRP
Desbloquea 17 modelos adicionales que hoy no caben en tu setup.
~$1,099 MSRP
Desbloquea 45 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 153% en los modelos que sí caben.
~$8,000 MSRP
RTX 3090 24GB (24 GB VRAM) can run these top models: Qwen3-VL 30B A3B Instruct (score: 96/100), Qwen3-Coder 30B A3B Instruct (score: 96/100), GPT-OSS 20B (score: 95/100). See the full compatibility list above.
RTX 3090 24GB has 24 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 3090 24GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 3090 24GB, we recommend Devstral Small 2 24B Instruct. It achieves 36.7 tokens per second with 40K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.
There are 5 upgrade path(s) from RTX 3090 24GB: RTX 3090 24GB, MacBook Pro M4 Max 36GB. Upgrading would unlock larger models and faster inference speeds.
Yes, RTX 3090 24GB with 24 GB of usable memory can run Flux.1 Dev at FP16 natively. Flux is a 12B parameter diffusion transformer that produces high-quality images. You can also run the Schnell variant for faster generation.
RTX 3090 24GB (24 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. Flux.1 Dev also runs natively for state-of-the-art image quality. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.
RTX 3090 24GB is excellent for AI image generation. With 24 GB of usable memory, it runs all major diffusion models including Flux.1, SDXL, and Stable Diffusion 3.5 at full precision. You can generate high-resolution images quickly and even handle video generation models.
Yes, RTX 3090 24GB with 24 GB of usable memory can run Qwen 3.5 27B at Q4_K_M (~16.5 GB) with ~7 GB headroom for context and runtime. Quality at Q4 is very close to full precision for most tasks. Run it with: ollama run qwen3.5:27b
With 24 GB on RTX 3090 24GB, Q4_K_M is the sweet spot for 27B-35B models, Q6_K for 14B models, and Q8_0 for 8B-9B models. The general rule: use the highest quantization that fits with at least 2-3 GB headroom for KV cache and runtime.
RTX 3090 24GB already has strong memory bandwidth, so the next limit is often memory capacity and context headroom rather than raw decode speed. For local LLMs, fit first and bandwidth second is the right mental model.
RTX 3090 24GB supports up to 2× GPU scaling via PCIe. With 2× GPUs, you get 48 GB effective memory with a 0.7× scaling factor per GPU. This enables running models like Qwen 3.5 397B A17B and Devstral 2 123B Instruct that don't fit on a single card.
RTX 3090 24GB uses PCIe for multi-GPU communication, which has approximately 30% scaling overhead. For best multi-GPU performance, consider NVLink-equipped variants.
Usually yes. If you want to run 2-4× RTX 3090 24GB for local AI, the bottleneck often becomes the platform, not the card. Workstation and server boards give you more CPU PCIe lanes, better x16 slot wiring, more spacing between cards, stronger power delivery, and usually more RAM capacity. Consumer x8/x8 layouts can work, but they are a common weak point in multi-GPU builds.
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