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 6000 Ada is the flagship single-GPU workstation card of the Ada Lovelace generation, packing 48 GB of ECC GDDR6 at 960 GB/s bandwidth with 91 TFLOPS FP16 compute. It is the workstation equivalent of the data-center A6000 Ada and can run 70B models at Q4 on a single card with usable throughput. For organizations needing the largest possible single-GPU VRAM footprint combined with ISV-certified drivers and ECC reliability, it represents the professional pinnacle before moving to multi-GPU or data-center hardware.
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) | Needs offload | 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.
Conselho de compra
Excelente escolha para IA local
Roda 29 de 50 modelos principais bem — um ótimo coringa para inferência local.
48.0 GB
VRAM
$6,800
Preço sugerido
$142/GB
Custo por GB de VRAM
Melhores modelos para esta 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 5 additional models that do not fit on the current setup.
Quer mais margem? AMD Instinct MI210 64GB (64.0 GB VRAM) é o próximo passo.
Cost vs cloud API
Assumes 4 hours/day of active inference at 100 tok/s, RTX 6000 Ada 48GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
43.2M
Tokens/month at this pace
$194
Monthly local cost
$432
Same tokens on cloud API
$4.50
Local $/1M tokens
Break-even: amortizes in 15.9 months vs cloud API. Price reference: $6.8k MSRP.
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, ollama, lm-studio.
RAG
SThis model is a direct match for rag. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
Qwen 3.6 35B A3BQwen3-Coder 30B A3B InstructQwen 3.5 35B A3BQwen3-VL 30B A3B InstructQwen 3 30B A3BQwen 3.5 27BQwen 3 32B
Magistral Small 2507Devstral Small 2 24B InstructNemotron Cascade 2 30B A3BNemotron 3 Nano 30BQwen 3.6 27BDevstral Small 1.1Qwen 3 14B
Gemma 4 31B
GPT-OSS 20B
Phi-4-reasoning-plus 14BQwen 3.5 9BGemma 4 26B A4BQwen 3 8B
EXAONE 4.0 32BQwen 3.5 4BMinistral 3 14BNemotron Nano 8BPhi-4 Mini Reasoning 4BQwen3-Coder-NextQwen 2.5 VL 72B
Jina Embeddings v3
BGE M3Qwen 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 111BGPT-OSS 120B
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2Qwen 3 235B A22BQwen3-Coder 480B A35B InstructLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BQuase ao alcance
Modelos de alta qualidade que precisam de um pouco mais de memória
Image & Video Generation
50 of 52 models can generate images or video on your RTX 6000 Ada 48GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 400ms | S |
| Stable Diffusion 1.5Image | 512×768 | 800ms | S |
| Realistic Vision v5.1Image | 512×768 | 800ms | S |
| DreamShaper 8Image | 512×768 | 800ms | S |
| LCM DreamShaper v7Image | 512×768 | 300ms | S |
| PixArt-SigmaImage | 1024×1024 | ~3.3s | S |
| FramePack I2VVideo | 640×480 | ~10.6s/frame | S |
| SDXL TurboImage | 512×512 | 400ms | S |
| SDXL LightningImage | 1024×1024 | ~1.3s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~3.3s | S |
| Playground v2.5Image | 1024×1024 | ~5s | S |
| RealVisXL v5.0Image | 1024×1024 | ~3.8s | S |
| DreamShaper XLImage | 1024×1024 | ~3.8s | S |
| Juggernaut XL v9Image | 1024×1024 | ~3.8s | S |
| Animagine XL 3.1Image | 1024×1024 | ~3.8s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~3.8s | S |
| Animagine XL 4.0Image | 1024×1024 | ~3.8s | S |
| Illustrious XLImage | 1024×1024 | ~3.8s | S |
| Wan Video 2.1 1.3BVideo | 480×832 | ~2.4s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~5.8s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~1s | S |
| LTX Video 2BVideo | 1280×720 | ~2.9s/frame | S |
| KolorsImage | 1024×1024 | ~6.7s | S |
| Stable CascadeImage | 1024×1024 | ~8.4s | S |
| AuraFlow v0.3Image | 1536×1536 | ~15s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~18.4s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~3.3s | S |
| CogVideoX 2BVideo | 720×480 | ~2.9s/frame | S |
| HunyuanVideoVideo | 256×256 | ~10.6s/frame | S |
| ChromaImage | 1024×1024 | ~3.3s | S |
| Z-Image TurboImage | 1536×1536 | ~3.4s | S |
| Flux.1 DevImage | 1024×1024 | ~15s | S |
| Flux.1 SchnellImage | 1024×1024 | ~2.9s | S |
| LTX Video 13BVideo | 768×512 | ~6.1s/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~16.7s | S |
| AnimateDiff v1.5.3Video | 512×768 | ~1.5s/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~4.8s/frame | S |
| CogVideoX 5BVideo | 720×480 | ~4.2s/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | ~4.2s/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | ~1.7s | S |
| Flux.1 Fill DevImage | 1024×1024 | ~14.2s | S |
| Mochi 1 PreviewVideo | 848×480 | ~5.5s/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | ~5.1s/frame | A |
| Helios 14BVideo | 832×480 | ~6.3s/frame | B |
| SkyReels V2 14BVideo | 256×256 | ~6.3s/frame | B |
| Wan Video 2.1 14BVideo | 256×256 | ~10.8s/frame | D |
| Wan Video 2.2 14BVideo | 256×256 | ~10.8s/frame | D |
| Qwen ImageImage | 256×256 | ~9.3s | D |
| Qwen Image EditImage | 256×256 | ~9.3s | D |
| Flux.2 DevImage | 256×256 | ~2m 38s | D |
| MAGI-1Video | 256×256 | ~7.8s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~9.9s | 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. NVLink provides 112.5 GB/s inter-GPU bandwidth with 22% overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× RTX | 48 GB | 338/374 | 960 GB/s |
| 2× RTX | 96 GB | 351/374 | 1,498 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.78× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Opções de upgrade
2× RTX 6000 Ada 48GBMulti-GPUUnlocks 13 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 16%.
Scale-out only pays off if the host platform has enough PCIe lanes, slot spacing, power, and cooling.
The bigger the setup gets, the more the runtime matters. Multi-GPU and multi-user serving are where vLLM, SGLang, TGI, TensorRT-LLM, or tuned llama.cpp start to earn their complexity.
~$6,800 MSRP
Unlocks 5 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 13%.
~$10,000 MSRP
NVIDIA A100 80GBUpgrade NVIDIAUnlocks 12 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 32%.
~$15,000 MSRP
Unlocks 13 additional models that do not fit on the current setup.
~$2,499 MSRP
Unlocks 26 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 112%.
~$8,000 MSRP
RTX 6000 Ada 48GB (48 GB VRAM) can run these top models: Qwen 3.6 35B A3B (score: 98/100), Qwen3-Coder 30B A3B Instruct (score: 97/100), Qwen 3.5 35B A3B (score: 96/100). See the full compatibility list above.
RTX 6000 Ada 48GB has 48 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 6000 Ada 48GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 6000 Ada 48GB, we recommend Qwen 3.6 27B. It achieves 33.9 tokens per second with 262K 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 5 upgrade path(s) from RTX 6000 Ada 48GB: RTX 6000 Ada 48GB, AMD Instinct MI210 64GB. Upgrading would unlock larger models and faster inference speeds.
Yes, RTX 6000 Ada 48GB with 48 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 6000 Ada 48GB (48 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 6000 Ada 48GB is excellent for AI image generation. With 48 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 6000 Ada 48GB with 48 GB of usable memory can run Qwen 3.5 27B at Q8 (near-lossless, ~28.9 GB) or even FP16 (~55.4 GB) depending on your context needs. This setup provides an excellent experience with this model. Use Ollama or vLLM for best results.
With 48 GB VRAM on RTX 6000 Ada 48GB, use Q8_0 for most models — it is near-lossless and you have the memory for it. For 70B+ models, Q6_K offers excellent quality. Reserve Q4_K_M for 100B+ models or when you need maximum context length.
RTX 6000 Ada 48GB 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 6000 Ada 48GB supports up to 2× GPU scaling via NVLink at 112.5 GB/s. With 2× GPUs, you get 96 GB effective memory with a 0.78× 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.
NVLink is recommended for RTX 6000 Ada 48GB multi-GPU inference, providing 112.5 GB/s interconnect bandwidth with only 22% scaling overhead. PCIe-only setups work but have higher overhead (~25%) due to limited inter-GPU bandwidth.
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