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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 | Detail |
|---|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
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.
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
SQwen 3.5 27B 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 3.6 27B 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, lm-studio.
Agentic Coding
SJust out of reach
High-quality models that need a bit more memory
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 v7 |
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
Upgrade options
Unlocks 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
Unlocks 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
Compare with similar
Related guides
| Needs offload |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~3.3s per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~15s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~18.4s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~2.9s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~8.5s/frame |
Qwen 3.6 27B 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, lm-studio.
Reasoning
SDevstral Small 2 24B Instruct 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
SQwen 3.5 27B matches RAG 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.
| 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.
Buying advice
Excellent choice for local AI
Runs 29 of 50 top models well — a strong all-rounder for local inference.
48.0 GB
VRAM
$6,800
MSRP
$142/GB
Cost per GB VRAM
Best models for this 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.
Want more headroom? AMD Instinct MI210 64GB (64.0 GB VRAM) is the next step up.
