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 NVIDIA GB200 Grace Blackwell Superchip is the per-module component of the GB200 NVL72 rack-scale system, pairing a Grace ARM CPU with two Blackwell B200 GPUs over 900 GB/s NVLink-C2C. Each module delivers 192 GB of HBM3e and up to 1,800 TFLOPS FP16, with FP4 Tensor Core support enabling even higher effective throughput for quantized inference. When combined in the GB200 NVL72 configuration — 72 GPUs acting as a single entity — the system delivers up to 30x more LLM inference throughput than an equivalent H100 cluster. This is NVIDIA's current cutting-edge platform for trillion-parameter model serving.
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) | Runs natively | 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) | Runs natively | 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.
Kaufberatung
Ausgezeichnete Wahl für lokale KI
Führt 40 von 50 Top-Modellen gut aus — ein starker Allrounder für lokale Inferenz.
192.0 GB
VRAM
$60,000
UVP
$313/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 4 additional models that do not fit on the current setup.
Mehr Spielraum gewünscht? AMD Instinct MI325X 256GB (256.0 GB VRAM) 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, 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 fits natively with comfortable headroom. Known channels: huggingface, lm-studio.
Devstral 2 123B Instruct
DeepSeek V4 Flash
Qwen 3.5 122B A10BMistral Small 4 119B
GPT-OSS 120BPixtral Large 124B
Command A 111BQwen 3 235B A22BQwen 2.5 VL 72BQwen3-Coder-NextQwen3-Coder 30B A3B InstructQwen 3.6 35B A3BLeanstral 119B A6BQwen 3.5 27BQwen3-VL 30B A3B InstructQwen 3.6 27BQwen 3.5 35B A3BQwen 3 32BMagistral Small 2507Devstral Small 2 24B InstructQwen 3 30B A3BQwen 3.5 9BNemotron 3 Nano 30BQwen 3 14BDevstral Small 1.1
Phi-4-reasoning-plus 14BQwen 3 8B
Gemma 4 31BGPT-OSS 20BNemotron Cascade 2 30B A3BQwen 3.5 4B
EXAONE 4.0 32BGemma 4 26B A4BMinistral 3 14BNemotron Nano 8BPhi-4 Mini Reasoning 4BDeepSeek Coder V2 236B
Jina Embeddings v3
BGE M3Qwen 3.5 397B A17B
Kimi K2.5Kimi K2.6DeepSeek V4 Pro
GLM-5.1GLM-5DeepSeek V3.2Qwen3-Coder 480B A35B InstructDeepSeek R1 671BDeepSeek V3.1 671BFast erreichbar
Hochwertige Modelle, die etwas mehr Speicher benötigen
Image & Video Generation
52 of 52 models can generate images or video on your NVIDIA GB200 192GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 0ms | S |
| Stable Diffusion 1.5Image | 512×768 | 0ms | S |
| Realistic Vision v5.1Image | 512×768 | 0ms | S |
| DreamShaper 8Image | 512×768 | 0ms | S |
| LCM DreamShaper v7Image | 512×768 | 0ms | S |
| PixArt-SigmaImage | 1024×1024 | 200ms | S |
| FramePack I2VVideo | 1280×720 | 300ms/frame | S |
| SDXL TurboImage | 512×512 | 0ms | S |
| SDXL LightningImage | 1024×1024 | 100ms | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | 200ms | S |
| Playground v2.5Image | 1024×1024 | 200ms | S |
| RealVisXL v5.0Image | 1024×1024 | 200ms | S |
| DreamShaper XLImage | 1024×1024 | 200ms | S |
| Juggernaut XL v9Image | 1024×1024 | 200ms | S |
| Animagine XL 3.1Image | 1024×1024 | 200ms | S |
| Pony Diffusion V6 XLImage | 1024×1024 | 200ms | S |
| Animagine XL 4.0Image | 1024×1024 | 200ms | S |
| Illustrious XLImage | 1024×1024 | 200ms | S |
| Wan Video 2.1 1.3BVideo | 480×832 | 100ms/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | 300ms | S |
| Flux.2 Klein 4BImage | 1024×1024 | 0ms | S |
| LTX Video 2BVideo | 1280×720 | 100ms/frame | S |
| KolorsImage | 1024×1024 | 300ms | S |
| Stable CascadeImage | 1024×1024 | 400ms | S |
| AuraFlow v0.3Image | 1536×1536 | 700ms | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | 900ms | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | 200ms | S |
| CogVideoX 2BVideo | 720×480 | 100ms/frame | S |
| HunyuanVideoVideo | 720×1280 | 300ms/frame | S |
| ChromaImage | 1024×1024 | 200ms | S |
| Z-Image TurboImage | 1536×1536 | 200ms | S |
| Flux.1 DevImage | 1024×1024 | 700ms | S |
| Flux.1 SchnellImage | 1024×1024 | 100ms | S |
| LTX Video 13BVideo | 1280×720 | 300ms/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | 800ms | S |
| AnimateDiff v1.5.3Video | 512×768 | 100ms/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | 200ms/frame | S |
| CogVideoX 5BVideo | 720×480 | 200ms/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | 200ms/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | 100ms | S |
| Flux.1 Fill DevImage | 1024×1024 | 700ms | S |
| Mochi 1 PreviewVideo | 848×480 | 300ms/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | 300ms/frame | S |
| Helios 14BVideo | 1280×720 | 300ms/frame | S |
| SkyReels V2 14BVideo | 1280×720 | 300ms/frame | S |
| Wan Video 2.1 14BVideo | 720×1280 | 300ms/frame | S |
| Wan Video 2.2 14BVideo | 720×1280 | 300ms/frame | S |
| Qwen ImageImage | 1024×1024 | 300ms | S |
| Qwen Image EditImage | 1024×1024 | 300ms | S |
| Flux.2 DevImage | 1024×1024 | ~7.8s | S |
| MAGI-1Video | 1280×720 | 400ms/frame | S |
| HunyuanImage 3.0Image | 1024×1024 | 500ms | B |
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 1800 GB/s inter-GPU bandwidth with 7% overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× NVIDIA | 192 GB | 359/374 | 8,000 GB/s |
| 2× NVIDIA | 384 GB | 366/374 | 14,880 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.93× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Upgrade-Optionen
2× NVIDIA GB200 192GBMulti-GPUUnlocks 7 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 27%.
NVLink gives this scale-out path a cleaner inter-GPU story than PCIe-only builds.
ca. $60,000 MSRP
Unlocks 4 additional models that do not fit on the current setup.
ca. $20,000 MSRP
Unlocks 5 additional models that do not fit on the current setup.
ca. $8,000 MSRP
NVIDIA GB200 192GB (192 GB VRAM) can run these top models: Devstral 2 123B Instruct (score: 96/100), DeepSeek V4 Flash (score: 96/100), Qwen 3.5 122B A10B (score: 95/100). See the full compatibility list above.
NVIDIA GB200 192GB has 192 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, NVIDIA GB200 192GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on NVIDIA GB200 192GB, we recommend Devstral 2 123B Instruct. It achieves 97.4 tokens per second with 256K 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 3 upgrade path(s) from NVIDIA GB200 192GB: NVIDIA GB200 192GB, AMD Instinct MI325X 256GB. Upgrading would unlock larger models and faster inference speeds.
Yes, NVIDIA GB200 192GB with 192 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.
NVIDIA GB200 192GB (192 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.
NVIDIA GB200 192GB is excellent for AI image generation. With 192 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, NVIDIA GB200 192GB with 192 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 192 GB VRAM on NVIDIA GB200 192GB, 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.
NVIDIA GB200 192GB 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.
NVIDIA GB200 192GB supports up to 2× GPU scaling via NVLink at 1800 GB/s. With 2× GPUs, you get 384 GB effective memory with a 0.93× scaling factor per GPU. This enables running models like Qwen 3.5 397B A17B and Kimi K2.5 that don't fit on a single card.
NVLink is recommended for NVIDIA GB200 192GB multi-GPU inference, providing 1800 GB/s interconnect bandwidth with only 7% scaling overhead. PCIe-only setups work but have higher overhead (~25%) due to limited inter-GPU bandwidth.
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