 |
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 H100 PCIe is the server-accessible variant of the H100 flagship, delivering 80 GB of HBM3 and full FP8 Transformer Engine support in a standard PCIe 5.0 form factor. Compared to the H100 SXM, it trades some bandwidth (2.0 TB/s vs. 3.35 TB/s) and compute (756 TFLOPS vs. 989 TFLOPS FP16) for compatibility with standard servers that lack SXM5 baseboard infrastructure. It remains a very capable inference GPU — able to run 70B models at FP16 and 4x faster than an A100 for LLM inference tasks. For teams that cannot afford SXM infrastructure, the H100 PCIe offers the Hopper Transformer Engine advantage in a drop-in form.
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
Hopper is NVIDIA's datacenter-focused architecture succeeding Ampere. Built on TSMC 4N, it introduces the Transformer Engine with automatic FP8/FP16 mixed-precision training, HBM3/HBM3e memory, and NVLink 4.0 for multi-GPU scaling. The H100 flagship delivers up to 3x the AI training performance of A100.
AI Relevance
The Transformer Engine automatically manages FP8 precision for optimal training speed without accuracy loss. With up to 141 GB HBM3e (H200), Hopper GPUs can hold the largest open-weight models entirely in GPU memory, making them the workhorse of AI datacenters.
Chat
SQwen 3 32B 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
SQwen3-Coder-Next 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, ollama, lm-studio.
Agentic Coding
SJust out of reach
High-quality models that need a bit more memory
Image & Video Generation
51 of 52 models can generate images or video on your NVIDIA H100 PCIe 80GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 0ms | S |
| Stable Diffusion 1.5Image | 512×768 | 100ms | S |
| Realistic Vision v5.1Image | 512×768 | 100ms | S |
| DreamShaper 8Image | 512×768 | 100ms | S |
| LCM DreamShaper v7 |
Multi-GPU scaling
Scale out with multiple GPUs for larger models. PCIe interconnect with 22% scaling overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× NVIDIA | 80 GB | 350/374 | 2,000 GB/s |
| 2× NVIDIA | 160 GB | 359/374 | 3,120 GB/s |
| 4× NVIDIA | 320 GB | 364/374 | 6,240 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 14 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 55%.
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.
~$30,000 MSRP
Unlocks 1 additional models that do not fit on the current setup.
~$3,999 MSRP
Unlocks 1 additional models that do not fit on the current setup.
~$9,999 MSRP
Unlocks 13 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 44%.
~$20,000 MSRP
Unlocks 14 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 61%.
~$8,000 MSRP
Compare with similar
| Runs natively |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~400ms per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~1.8s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~2.2s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~300ms/frame |
| Video Long (100f) | Runs with offload | Wan Video 14B | ~~1s/frame |
Qwen3-Coder-Next 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, ollama, lm-studio.
Reasoning
SQwen3-Coder-Next 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 |
| 0ms |
| S |
| PixArt-SigmaImage | 1024×1024 | 400ms | S |
| FramePack I2VVideo | 1280×720 | 700ms/frame | S |
| SDXL TurboImage | 512×512 | 0ms | S |
| SDXL LightningImage | 1024×1024 | 100ms | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | 400ms | S |
| Playground v2.5Image | 1024×1024 | 600ms | S |
| RealVisXL v5.0Image | 1024×1024 | 400ms | S |
| DreamShaper XLImage | 1024×1024 | 400ms | S |
| Juggernaut XL v9Image | 1024×1024 | 400ms | S |
| Animagine XL 3.1Image | 1024×1024 | 400ms | S |
| Pony Diffusion V6 XLImage | 1024×1024 | 400ms | S |
| Animagine XL 4.0Image | 1024×1024 | 400ms | S |
| Illustrious XLImage | 1024×1024 | 400ms | S |
| Wan Video 2.1 1.3BVideo | 480×832 | 300ms/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | 700ms | S |
| Flux.2 Klein 4BImage | 1024×1024 | 100ms | S |
| LTX Video 2BVideo | 1280×720 | 300ms/frame | S |
| KolorsImage | 1024×1024 | 800ms | S |
| Stable CascadeImage | 1024×1024 | ~1s | S |
| AuraFlow v0.3Image | 1536×1536 | ~1.8s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~2.2s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | 400ms | S |
| CogVideoX 2BVideo | 720×480 | 300ms/frame | S |
| HunyuanVideoVideo | 720×1280 | 700ms/frame | S |
| ChromaImage | 1024×1024 | 400ms | S |
| Z-Image TurboImage | 1536×1536 | 400ms | S |
| Flux.1 DevImage | 1024×1024 | ~1.8s | S |
| Flux.1 SchnellImage | 1024×1024 | 300ms | S |
| LTX Video 13BVideo | 1280×720 | 700ms/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~2s | S |
| AnimateDiff v1.5.3Video | 512×768 | 200ms/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | 600ms/frame | S |
| CogVideoX 5BVideo | 720×480 | 500ms/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | 500ms/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | 200ms | S |
| Flux.1 Fill DevImage | 1024×1024 | ~1.7s | S |
| Mochi 1 PreviewVideo | 848×480 | 600ms/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | 600ms/frame | S |
| Helios 14BVideo | 1280×720 | 700ms/frame | S |
| SkyReels V2 14BVideo | 1280×720 | 700ms/frame | S |
| Wan Video 2.1 14BVideo | 720×1280 | 700ms/frame | S |
| Wan Video 2.2 14BVideo | 720×1280 | 700ms/frame | S |
| Qwen ImageImage | 1024×1024 | 700ms | S |
| Qwen Image EditImage | 1024×1024 | 700ms | S |
| Flux.2 DevImage | 1024×1024 | ~18.6s | S |
| MAGI-1Video | 1280×720 | 900ms/frame | A |
| HunyuanImage 3.0Image | 256×256 | ~1.2s | 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.
Buying advice
Excellent choice for local AI
Runs 36 of 50 top models well — a strong all-rounder for local inference.
80.0 GB
VRAM
$30,000
MSRP
$375/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 1 additional models that do not fit on the current setup.
Want more headroom? Mac Studio M2 Ultra 128GB (128.0 GB unified memory) is the next step up.
