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 4000 Ada brings 20 GB of ECC GDDR6 to the mid-range workstation segment — 4 GB more than any consumer Ada card at a comparable price tier. Built on Ada Lovelace with full professional driver support, it is well suited for sustained 13B inference and can handle many 30B models at Q4 quantization. The $1,250 price positions it as a practical workhorse for AI-enabled professional workstations that need certified reliability alongside genuine VRAM headroom.
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) | Needs offload | 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) | Very constrained | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Tight fit | 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
Gut für lokale KI
Bewältigt 21 von 50 Top-Modellen. Kleinere und mittelgroße Modelle laufen komfortabel.
20.0 GB
VRAM
$1,250
UVP
$63/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 17 additional models that do not fit on the current setup.
Mehr Spielraum gewünscht? MacBook Pro M1 Max 32GB (32.0 GB unified memory) ist die nächste Stufe.
Chat
SQwen 3 14B 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
SQwen 3.5 9B is a specialized fit for 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.
Agentic Coding
SQwen 3.5 9B 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
SQwen 3 14B matches Reasoning 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.
RAG
AGranite 4.1 8B 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.
Qwen 3 14B
Phi-4-reasoning-plus 14BQwen 3.5 9BQwen 3 8B
GPT-OSS 20B
Magistral Small 2507Devstral Small 2 24B InstructQwen 3.6 27BMinistral 3 14BDevstral Small 1.1Qwen 3.5 4BNemotron Nano 8BPhi-4 Mini Reasoning 4BQwen3-Coder 30B A3B InstructQwen3-VL 30B A3B InstructQwen 3 30B A3BQwen 3.5 27B
Jina Embeddings v3
BGE M3
Gemma 4 26B A4BNemotron 3 Nano 30BQwen 3.5 397B A17BDevstral 2 123B Instruct
Kimi K2.5Kimi K2.6
DeepSeek V4 ProQwen 3.5 122B A10BQwen 3.6 35B A3BDeepSeek V4 FlashQwen 3.5 35B A3BQwen 3 32BMistral 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 InstructNemotron Cascade 2 30B A3BGemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671B
EXAONE 4.0 32BFast erreichbar
Hochwertige Modelle, die etwas mehr Speicher benötigen
Image & Video Generation
39 of 52 models can generate images or video on your RTX 4000 Ada 20GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~1.5s | S |
| Stable Diffusion 1.5Image | 512×768 | ~3s | S |
| Realistic Vision v5.1Image | 512×768 | ~3s | S |
| DreamShaper 8Image | 512×768 | ~3s | S |
| LCM DreamShaper v7Image | 512×768 | 900ms | S |
| PixArt-SigmaImage | 1024×1024 | ~11.8s | S |
| FramePack I2VVideo | 256×256 | ~21.7s/frame | S |
| SDXL TurboImage | 512×512 | ~1.5s | S |
| SDXL LightningImage | 1024×1024 | ~4.4s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~11.8s | S |
| Playground v2.5Image | 1024×1024 | ~17.8s | S |
| RealVisXL v5.0Image | 1024×1024 | ~13.3s | S |
| DreamShaper XLImage | 1024×1024 | ~13.3s | S |
| Juggernaut XL v9Image | 1024×1024 | ~13.3s | S |
| Animagine XL 3.1Image | 1024×1024 | ~13.3s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~13.3s | S |
| Animagine XL 4.0Image | 1024×1024 | ~13.3s | S |
| Illustrious XLImage | 1024×1024 | ~13.3s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~8.7s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~20.7s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~3.6s | S |
| LTX Video 2BVideo | 512×512 | ~30.8s/frame | S |
| KolorsImage | 1024×1024 | ~23.7s | S |
| Stable CascadeImage | 1024×1024 | ~29.6s | S |
| AuraFlow v0.3Image | 1536×1536 | ~53.3s | A |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~1m 5s | A |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~11.8s | A |
| CogVideoX 2BVideo | 256×256 | ~30.8s/frame | B |
| HunyuanVideoVideo | 256×256 | ~21.7s/frame | B |
| ChromaImage | 256×256 | ~11.8s | B |
| Z-Image TurboImage | 256×256 | ~24.4s | B |
| Flux.1 DevImage | 256×256 | ~53.3s | D |
| Flux.1 SchnellImage | 256×256 | ~10.4s | D |
| LTX Video 13BVideo | 256×256 | ~21.7s/frame | D |
| Flux.1 Kontext DevImage | 256×256 | ~59.2s | D |
| AnimateDiff v1.5.3Video | 512×768 | ~5.4s/frame | D |
| Cosmos Diffusion 7BVideo | 256×256 | ~32.7s/frame | D |
| CogVideoX 5BVideo | 256×256 | ~31.1s/frame | D |
| Wan2.2 TI2V 5BVideo | 256×256 | ~31.1s/frame | D |
| Flux.2 Klein 9BImage | 256×256 | ~5.9s | F |
| Flux.1 Fill DevImage | 256×256 | ~50.3s | F |
| Mochi 1 PreviewVideo | 256×256 | ~19.6s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~18.2s/frame | F |
| Helios 14BVideo | 256×256 | ~22.4s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~22.4s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~22.4s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~22.4s/frame | F |
| Qwen ImageImage | 256×256 | ~19.9s | F |
| Qwen Image EditImage | 256×256 | ~19.9s | F |
| Flux.2 DevImage | 256×256 | ~9m 20s | F |
| MAGI-1Video | 256×256 | ~27.8s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~35.1s | 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 17 additional models that do not fit on the current setup.
ca. $2,499 MSRP
Intel Arc Pro B60 24GBBestes Preis-Leistungs-VerhältnisUnlocks 22 additional models that do not fit on the current setup.
ca. $599 MSRP
RTX 3090 Ti 24GBNVIDIA-UpgradeUnlocks 22 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 43%.
ca. $1,999 MSRP
Unlocks 67 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 250%.
ca. $8,000 MSRP
RTX 4000 Ada 20GB (20 GB VRAM) can run these top models: Qwen 3 14B (score: 94/100), Phi-4-reasoning-plus 14B (score: 93/100), Qwen 3.5 9B (score: 93/100). See the full compatibility list above.
RTX 4000 Ada 20GB has 20 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 4000 Ada 20GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 4000 Ada 20GB, we recommend Qwen 3.5 9B. It achieves 42.9 tokens per second with 71K context window. Qwen 3.5 9B is a specialized fit for 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.
There are 4 upgrade path(s) from RTX 4000 Ada 20GB: MacBook Pro M1 Max 32GB, Intel Arc Pro B60 24GB. Upgrading would unlock larger models and faster inference speeds.
RTX 4000 Ada 20GB can run Flux.1 Dev with sequential offloading or at a lower precision (FP8/NF4). The Schnell variant is faster and fits more easily. For best results, use ComfyUI with model offloading enabled.
RTX 4000 Ada 20GB (20 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.
RTX 4000 Ada 20GB is good for AI image generation. It handles SDXL and SD 3.5 well, and can run Flux with some optimization. 20 GB of usable memory is sufficient for most image generation workflows at standard resolutions.
Qwen 3.5 27B needs ~16.5 GB at Q4_K_M, which is tight for RTX 4000 Ada 20GB with 20 GB. You can run the 9B variant at Q8 (9.6 GB) for excellent quality, or try the 35B-A3B MoE variant at Q4 if it fits your context needs.
With 20 GB on RTX 4000 Ada 20GB, use Q8_0 for 8B models (best quality), Q4_K_M for 14B models (good balance), and Q4_K_M with limited context for larger models. Avoid going below Q4 — quality drops sharply at Q2-Q3.
RTX 4000 Ada 20GB 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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