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 PRO 5000 Blackwell delivers 48 GB of ECC GDDR7 at 1,344 GB/s bandwidth with 96 TFLOPS FP16 and 2,500 INT8 TOPS — a major generational leap over the RTX 6000 Ada in both compute and memory bandwidth. Announced at GTC 2025 and shipping summer 2025, it comfortably handles 70B quantized inference on a single card and can support larger models with NVLink pairing. For professional AI workstations requiring maximum VRAM, ECC reliability, and certified driver support short of the flagship 96 GB tier, this is the sweet spot.
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
Blackwell is NVIDIA's fifth-generation RTX architecture, built on TSMC's 4NP process. It introduces 5th-generation Tensor Cores with native FP4 precision support, enabling double the inference throughput per watt compared to Ada Lovelace's FP8 operations. Key innovations include the Neural Rendering Pipeline for AI-driven shading and the debut of GDDR7 memory in consumer GPUs.
AI Relevance
FP4 Tensor Cores deliver the highest tokens-per-watt efficiency in any consumer architecture. Native FP4 quantization means models can run at lower precision with minimal quality loss, effectively doubling the effective VRAM for model weights.
購入アドバイス
ローカルAIに最適な選択
上位50モデル中29モデルを快適に実行 — ローカル推論の万能選手です。
48.0 GB
VRAM
$4,999
希望小売価格
$104/GB
GBあたりのコスト
このGPUに最適なモデル
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.
もっと余裕が欲しいですか？ AMD Instinct MI210 64GB (64.0 GB VRAM) が次のステップアップです。
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 3.5 27BQwen 3 32BQwen 3 30B A3B
Magistral Small 2507Devstral Small 2 24B InstructNemotron 3 Nano 30BQwen 3.6 27BNemotron Cascade 2 30B A3BDevstral Small 1.1
Gemma 4 31BQwen 3 14B
GPT-OSS 20B
Phi-4-reasoning-plus 14BQwen 3.5 9B
EXAONE 4.0 32BGemma 4 26B A4BQwen 3 8BQwen 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 671Bもう少しで届く
もう少しメモリがあれば動く高品質モデル
Image & Video Generation
50 of 52 models can generate images or video on your RTX PRO 5000 Blackwell 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 | 200ms | S |
| PixArt-SigmaImage | 1024×1024 | ~3.1s | S |
| FramePack I2VVideo | 640×480 | ~9.8s/frame | S |
| SDXL TurboImage | 512×512 | 400ms | S |
| SDXL LightningImage | 1024×1024 | ~1.2s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~3.1s | S |
| Playground v2.5Image | 1024×1024 | ~4.6s | S |
| RealVisXL v5.0Image | 1024×1024 | ~3.5s | S |
| DreamShaper XLImage | 1024×1024 | ~3.5s | S |
| Juggernaut XL v9Image | 1024×1024 | ~3.5s | S |
| Animagine XL 3.1Image | 1024×1024 | ~3.5s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~3.5s | S |
| Animagine XL 4.0Image | 1024×1024 | ~3.5s | S |
| Illustrious XLImage | 1024×1024 | ~3.5s | S |
| Wan Video 2.1 1.3BVideo | 480×832 | ~2.3s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~5.4s | S |
| Flux.2 Klein 4BImage | 1024×1024 | 900ms | S |
| LTX Video 2BVideo | 1280×720 | ~2.7s/frame | S |
| KolorsImage | 1024×1024 | ~6.2s | S |
| Stable CascadeImage | 1024×1024 | ~7.7s | S |
| AuraFlow v0.3Image | 1536×1536 | ~13.9s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~17s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~3.1s | S |
| CogVideoX 2BVideo | 720×480 | ~2.7s/frame | S |
| HunyuanVideoVideo | 256×256 | ~9.8s/frame | S |
| ChromaImage | 1024×1024 | ~3.1s | S |
| Z-Image TurboImage | 1536×1536 | ~3.2s | S |
| Flux.1 DevImage | 1024×1024 | ~13.9s | S |
| Flux.1 SchnellImage | 1024×1024 | ~2.7s | S |
| LTX Video 13BVideo | 768×512 | ~5.7s/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~15.5s | S |
| AnimateDiff v1.5.3Video | 512×768 | ~1.4s/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~4.4s/frame | S |
| CogVideoX 5BVideo | 720×480 | ~3.9s/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | ~3.9s/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | ~1.5s | S |
| Flux.1 Fill DevImage | 1024×1024 | ~13.1s | S |
| Mochi 1 PreviewVideo | 848×480 | ~5.1s/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | ~4.7s/frame | A |
| Helios 14BVideo | 832×480 | ~5.8s/frame | B |
| SkyReels V2 14BVideo | 256×256 | ~5.8s/frame | B |
| Wan Video 2.1 14BVideo | 256×256 | ~10s/frame | D |
| Wan Video 2.2 14BVideo | 256×256 | ~10s/frame | D |
| Qwen ImageImage | 256×256 | ~8.6s | D |
| Qwen Image EditImage | 256×256 | ~8.6s | D |
| Flux.2 DevImage | 256×256 | ~2m 26s | D |
| MAGI-1Video | 256×256 | ~7.3s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~9.2s | 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
アップグレードオプション
Unlocks 5 additional models that do not fit on the current setup.
〜$10,000 MSRP
NVIDIA A100 80GBNVIDIAアップグレードUnlocks 12 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 16%.
〜$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 86%.
〜$8,000 MSRP
RTX PRO 5000 Blackwell 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 PRO 5000 Blackwell 48GB has 48 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX PRO 5000 Blackwell 48GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX PRO 5000 Blackwell 48GB, we recommend Qwen 3.6 27B. It achieves 46.1 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 4 upgrade path(s) from RTX PRO 5000 Blackwell 48GB: AMD Instinct MI210 64GB, NVIDIA A100 80GB. Upgrading would unlock larger models and faster inference speeds.
Yes, RTX PRO 5000 Blackwell 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 PRO 5000 Blackwell 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 PRO 5000 Blackwell 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 PRO 5000 Blackwell 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 PRO 5000 Blackwell 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 PRO 5000 Blackwell 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.
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