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 5000 Ada is one of the few single-slot workstation GPUs offering 32 GB of ECC GDDR6, making it the entry point for running 70B quantized models on a single workstation card. With 52 TFLOPS FP16 and full Ada FP8 support, it offers meaningful inference throughput alongside the reliability guarantees of professional drivers. At $4,000 it occupies a niche between the RTX 4500 Ada and RTX 6000 Ada for teams that need more VRAM than 24 GB but cannot justify the 48 GB flagship price.
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 natively | 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
Ada Lovelace is NVIDIA's fourth-generation RTX architecture, manufactured on TSMC's custom 4N process. It introduces 4th-generation Tensor Cores with FP8 support, 3rd-generation ray tracing cores, and the Shader Execution Reordering (SER) engine for improved workload scheduling.
AI Relevance
FP8 Tensor Core operations provide a significant uplift for quantized LLM inference compared to Ampere's FP16-only Tensor Cores. DLSS 3 Frame Generation demonstrates the architecture's AI processing capabilities.
Kaufberatung
Ausgezeichnete Wahl für lokale KI
Führt 27 von 50 Top-Modellen gut aus — ein starker Allrounder für lokale Inferenz.
32.0 GB
VRAM
$4,000
UVP
$125/GB
Kosten pro GB VRAM
Beste Modelle für diese 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 11 additional models that do not fit on the current setup.
Mehr Spielraum gewünscht? MacBook Pro M1 Max 64GB (64.0 GB unified memory) ist die nächste Stufe.
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 fits natively with comfortable headroom. Known channels: huggingface, 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 fits natively with comfortable headroom. 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, lm-studio.
RAG
SThis model is a direct match for rag. 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.
Qwen3-Coder 30B A3B InstructQwen3-VL 30B A3B InstructQwen 3 30B A3BQwen 3.5 27BQwen 3.6 35B A3B
Magistral Small 2507Qwen 3.6 27BQwen 3.5 35B A3BDevstral Small 2 24B InstructNemotron Cascade 2 30B A3B
GPT-OSS 20BNemotron 3 Nano 30BDevstral Small 1.1Qwen 3.5 9B
Gemma 4 26B A4BQwen 3 14BQwen 3 32B
Phi-4-reasoning-plus 14BQwen 3 8BQwen 3.5 4BMinistral 3 14B
EXAONE 4.0 32BNemotron Nano 8BPhi-4 Mini Reasoning 4B
Jina Embeddings v3
BGE M3Gemma 4 31BQwen 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 InstructLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BFast erreichbar
Hochwertige Modelle, die etwas mehr Speicher benötigen
Image & Video Generation
43 of 52 models can generate images or video on your RTX 5000 Ada 32GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 700ms | S |
| Stable Diffusion 1.5Image | 512×768 | ~1.5s | S |
| Realistic Vision v5.1Image | 512×768 | ~1.5s | S |
| DreamShaper 8Image | 512×768 | ~1.5s | S |
| LCM DreamShaper v7Image | 512×768 | 400ms | S |
| PixArt-SigmaImage | 1024×1024 | ~6s | S |
| FramePack I2VVideo | 256×256 | ~11s/frame | S |
| SDXL TurboImage | 512×512 | 700ms | S |
| SDXL LightningImage | 1024×1024 | ~2.2s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~6s | S |
| Playground v2.5Image | 1024×1024 | ~9s | S |
| RealVisXL v5.0Image | 1024×1024 | ~6.7s | S |
| DreamShaper XLImage | 1024×1024 | ~6.7s | S |
| Juggernaut XL v9Image | 1024×1024 | ~6.7s | S |
| Animagine XL 3.1Image | 1024×1024 | ~6.7s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~6.7s | S |
| Animagine XL 4.0Image | 1024×1024 | ~6.7s | S |
| Illustrious XLImage | 1024×1024 | ~6.7s | S |
| Wan Video 2.1 1.3BVideo | 480×832 | ~4.4s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~10.5s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~1.8s | S |
| LTX Video 2BVideo | 1280×720 | ~5.2s/frame | S |
| KolorsImage | 1024×1024 | ~12s | S |
| Stable CascadeImage | 1024×1024 | ~15s | S |
| AuraFlow v0.3Image | 1536×1536 | ~27s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~33s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~6s | S |
| CogVideoX 2BVideo | 720×480 | ~5.2s/frame | S |
| HunyuanVideoVideo | 256×256 | ~11s/frame | S |
| ChromaImage | 1024×1024 | ~6s | S |
| Z-Image TurboImage | 1536×1536 | ~6.2s | S |
| Flux.1 DevImage | 256×256 | ~47.2s | S |
| Flux.1 SchnellImage | 256×256 | ~9.2s | S |
| LTX Video 13BVideo | 256×256 | ~11s/frame | S |
| Flux.1 Kontext DevImage | 256×256 | ~52.4s | S |
| AnimateDiff v1.5.3Video | 512×768 | ~2.7s/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~8.6s/frame | A |
| CogVideoX 5BVideo | 720×480 | ~7.5s/frame | A |
| Wan2.2 TI2V 5BVideo | 832×480 | ~7.5s/frame | A |
| Flux.2 Klein 9BImage | 1024×1024 | ~3s | A |
| Flux.1 Fill DevImage | 256×256 | ~44.6s | B |
| Mochi 1 PreviewVideo | 256×256 | ~17.8s/frame | D |
| HunyuanVideo 1.5Video | 256×256 | ~17.1s/frame | D |
| Helios 14BVideo | 256×256 | ~11.3s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~11.3s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~11.3s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~11.3s/frame | F |
| Qwen ImageImage | 256×256 | ~10.1s | F |
| Qwen Image EditImage | 256×256 | ~10.1s | F |
| Flux.2 DevImage | 256×256 | ~4m 44s | F |
| MAGI-1Video | 256×256 | ~14.1s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~17.8s | 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
Upgrade-Optionen
Unlocks 11 additional models that do not fit on the current setup.
ca. $2,499 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIA-UpgradeUnlocks 13 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 37%.
ca. $4,999 MSRP
Unlocks 26 additional models that do not fit on the current setup.
ca. $2,499 MSRP
Unlocks 39 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 155%.
ca. $8,000 MSRP
RTX 5000 Ada 32GB (32 GB VRAM) can run these top models: Qwen3-Coder 30B A3B Instruct (score: 99/100), Qwen3-VL 30B A3B Instruct (score: 98/100), Qwen 3 30B A3B (score: 97/100). See the full compatibility list above.
RTX 5000 Ada 32GB has 32 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 5000 Ada 32GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 5000 Ada 32GB, we recommend Qwen 3.6 27B. It achieves 23.0 tokens per second with 187K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, lm-studio.
There are 4 upgrade path(s) from RTX 5000 Ada 32GB: MacBook Pro M1 Max 64GB, RTX PRO 5000 Blackwell 48GB. Upgrading would unlock larger models and faster inference speeds.
Yes, RTX 5000 Ada 32GB with 32 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 5000 Ada 32GB (32 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 5000 Ada 32GB is excellent for AI image generation. With 32 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 5000 Ada 32GB with 32 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 32 GB on RTX 5000 Ada 32GB, 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 5000 Ada 32GB has enough memory for many local LLMs, but bandwidth still matters a lot for real speed. Once a model fits, a faster-memory GPU can feel significantly better than a slower setup with similar capacity.
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