Chat
SMistral Small 4 119B
This 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.
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VOOZH | about |
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 B200 is the flagship Blackwell datacenter GPU, delivering 180 GB of HBM3e and 2,250 TFLOPS of FP16 compute โ roughly 2.3x the compute of an H100 at over twice the VRAM. Its new fourth-generation Tensor Cores add FP4 support, enabling up to 4,500 TOPS for FP4 inference, and the Blackwell architecture introduces a second-generation Transformer Engine. A single B200 can serve 70B models at FP16 with headroom for large batch sizes and long context windows, making it suitable for high-throughput production inference. At approximately 1,000W TDP, it targets next-generation liquid-cooled infrastructure.
Beyond LLMs
What AI tasks this GPU can handle โ from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | โ |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 | โ |
| LLM Large (70B) | Runs natively | Llama 3.1 70B Q4 | โ |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~100ms per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~600ms per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~700ms per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~100ms/frame |
| Video Long (100f) | Runs natively | Wan Video 14B | ~300ms/frame |
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.
Buying advice
Excellent choice for local AI
Runs 39 of 50 top models well โ a strong all-rounder for local inference.
180.0 GB
VRAM
$30,000
MSRP
$167/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 4 additional models that do not fit on the current setup.
Want more headroom? AMD Instinct MI325X 256GB (256.0 GB VRAM) is the next step up.
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.
Just out of reach
High-quality models that need a bit more memory
Image & Video Generation
52 of 52 models can generate images or video on your NVIDIA B200 180GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512ร512 | 0ms | S |
| Stable Diffusion 1.5Image | 512ร768 | 0ms | S |
| Realistic Vision v5.1Image | 512ร768 | 0ms | S |
| DreamShaper 8Image | 512ร768 | 0ms | S |
| LCM DreamShaper v7Image | 512ร768 | 0ms | S |
| PixArt-SigmaImage | 1024ร1024 | 100ms | S |
| FramePack I2VVideo | 1280ร720 | 200ms/frame | S |
| SDXL TurboImage | 512ร512 | 0ms | S |
| SDXL LightningImage | 1024ร1024 | 0ms | S |
| Stable Diffusion XL 1.0Image | 1024ร1024 | 100ms | S |
| Playground v2.5Image | 1024ร1024 | 200ms | S |
| RealVisXL v5.0Image | 1024ร1024 | 100ms | S |
| DreamShaper XLImage | 1024ร1024 | 100ms | S |
| Juggernaut XL v9Image | 1024ร1024 | 100ms | S |
| Animagine XL 3.1Image | 1024ร1024 | 100ms | S |
| Pony Diffusion V6 XLImage | 1024ร1024 | 100ms | S |
| Animagine XL 4.0Image | 1024ร1024 | 100ms | S |
| Illustrious XLImage | 1024ร1024 | 100ms | S |
| Wan Video 2.1 1.3BVideo | 480ร832 | 100ms/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024ร1024 | 200ms | S |
| Flux.2 Klein 4BImage | 1024ร1024 | 0ms | S |
| LTX Video 2BVideo | 1280ร720 | 100ms/frame | S |
| KolorsImage | 1024ร1024 | 300ms | S |
| Stable CascadeImage | 1024ร1024 | 300ms | S |
| AuraFlow v0.3Image | 1536ร1536 | 600ms | S |
| Stable Diffusion 3.5 LargeImage | 1024ร1024 | 700ms | S |
| Stable Diffusion 3.5 Large TurboImage | 1024ร1024 | 100ms | S |
| CogVideoX 2BVideo | 720ร480 | 100ms/frame | S |
| HunyuanVideoVideo | 720ร1280 | 200ms/frame | S |
| ChromaImage | 1024ร1024 | 100ms | S |
| Z-Image TurboImage | 1536ร1536 | 100ms | S |
| Flux.1 DevImage | 1024ร1024 | 600ms | S |
| Flux.1 SchnellImage | 1024ร1024 | 100ms | S |
| LTX Video 13BVideo | 1280ร720 | 200ms/frame | S |
| Flux.1 Kontext DevImage | 1024ร1024 | 700ms | S |
| AnimateDiff v1.5.3Video | 512ร768 | 100ms/frame | S |
| Cosmos Diffusion 7BVideo | 1024ร576 | 200ms/frame | S |
| CogVideoX 5BVideo | 720ร480 | 200ms/frame | S |
| Wan2.2 TI2V 5BVideo | 832ร480 | 200ms/frame | S |
| Flux.2 Klein 9BImage | 1024ร1024 | 100ms | S |
| Flux.1 Fill DevImage | 1024ร1024 | 600ms | S |
| Mochi 1 PreviewVideo | 848ร480 | 200ms/frame | S |
| HunyuanVideo 1.5Video | 720ร1280 | 200ms/frame | S |
| Helios 14BVideo | 1280ร720 | 200ms/frame | S |
| SkyReels V2 14BVideo | 1280ร720 | 200ms/frame | S |
| Wan Video 2.1 14BVideo | 720ร1280 | 200ms/frame | S |
| Wan Video 2.2 14BVideo | 720ร1280 | 200ms/frame | S |
| Qwen ImageImage | 1024ร1024 | 200ms | S |
| Qwen Image EditImage | 1024ร1024 | 200ms | S |
| Flux.2 DevImage | 1024ร1024 | ~6.2s | S |
| MAGI-1Video | 1280ร720 | 300ms/frame | S |
| HunyuanImage 3.0Image | 1024ร1024 | 400ms | B |
Image models estimated at 1024ร1024 (28 steps, FP16). Video models estimated at 768ร512 (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 |
|---|---|---|---|
| 1ร NVIDIA | 180 GB | 359/374 | 8,000 GB/s |
| 2ร NVIDIA | 360 GB | 364/374 | 14,880 GB/s |
| 4ร NVIDIA | 720 GB | 373/374 | 29,760 GB/s |
| 8ร NVIDIA | 1440 GB | 374/374 | 59,520 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.93ร per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Upgrade options
Unlocks 15 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 171%.
NVLink gives this scale-out path a cleaner inter-GPU story than PCIe-only builds.
~$30,000 MSRP
Unlocks 4 additional models that do not fit on the current setup.
~$20,000 MSRP
Unlocks 5 additional models that do not fit on the current setup.
~$8,000 MSRP
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