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 H200 SXM is the upgraded Hopper flagship with 141 GB of HBM3e memory — nearly double the H100's capacity — making it the first single GPU capable of running 70B models at FP16 with substantial KV cache headroom. Its 4,800 GB/s bandwidth is 43% higher than the H100 SXM, delivering proportionally faster token generation for memory-bandwidth-bound inference. The compute core is identical to the H100 (same die, same TFLOPS), so the H200's primary advantage is memory capacity and bandwidth rather than raw compute.
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
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.
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38モデルを快適に実行 — ローカル推論の万能選手です。
141.0 GB
VRAM
$30,000
希望小売価格
$213/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 6 additional models that do not fit on the current setup.
もっと余裕が欲しいですか？ NVIDIA B200 180GB (180.0 GB VRAM) が次のステップアップです。
Cost vs cloud API
Assumes 4 hours/day of active inference at 162 tok/s, NVIDIA H200 141GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
70.0M
Tokens/month at this pace
$985
Monthly local cost
$700
Same tokens on cloud API
$14.1
Local $/1M tokens
Break-even: long amortization at this workload — local is still the privacy/latency play. Price reference: $35.0k MSRP.
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.
Qwen 3.5 122B A10B
Devstral 2 123B InstructMistral Small 4 119B
GPT-OSS 120BPixtral Large 124B
Command A 111BLeanstral 119B A6BQwen 2.5 VL 72BQwen3-Coder-NextQwen3-Coder 30B A3B InstructQwen 3.6 35B A3BQwen 3.5 27BQwen3-VL 30B A3B InstructQwen 3.6 27BQwen 3.5 35B A3BQwen 3 32BMagistral Small 2507Devstral Small 2 24B InstructQwen 3 30B A3BNemotron 3 Nano 30BQwen 3.5 9BQwen 3 14BDevstral Small 1.1
Gemma 4 31B
Phi-4-reasoning-plus 14BQwen 3 8BNemotron Cascade 2 30B A3BGPT-OSS 20BQwen 3.5 4B
EXAONE 4.0 32BGemma 4 26B A4BMinistral 3 14BQwen 3 235B A22BNemotron Nano 8BPhi-4 Mini Reasoning 4B
Jina Embeddings v3
BGE M3Qwen 3.5 397B A17B
Kimi K2.5Kimi K2.6
DeepSeek V4 ProDeepSeek V4 Flash
GLM-5.1GLM-5DeepSeek V3.2Qwen3-Coder 480B A35B InstructDeepSeek 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
52 of 52 models can generate images or video on your NVIDIA H200 141GB
| 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 v7Image | 512×768 | 0ms | S |
| PixArt-SigmaImage | 1024×1024 | 300ms | S |
| FramePack I2VVideo | 1280×720 | 600ms/frame | S |
| SDXL TurboImage | 512×512 | 0ms | S |
| SDXL LightningImage | 1024×1024 | 100ms | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | 300ms | S |
| Playground v2.5Image | 1024×1024 | 500ms | S |
| RealVisXL v5.0Image | 1024×1024 | 300ms | S |
| DreamShaper XLImage | 1024×1024 | 300ms | S |
| Juggernaut XL v9Image | 1024×1024 | 300ms | S |
| Animagine XL 3.1Image | 1024×1024 | 300ms | S |
| Pony Diffusion V6 XLImage | 1024×1024 | 300ms | S |
| Animagine XL 4.0Image | 1024×1024 | 300ms | S |
| Illustrious XLImage | 1024×1024 | 300ms | S |
| Wan Video 2.1 1.3BVideo | 480×832 | 200ms/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | 500ms | S |
| Flux.2 Klein 4BImage | 1024×1024 | 100ms | S |
| LTX Video 2BVideo | 1280×720 | 300ms/frame | S |
| KolorsImage | 1024×1024 | 600ms | S |
| Stable CascadeImage | 1024×1024 | 800ms | S |
| AuraFlow v0.3Image | 1536×1536 | ~1.4s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~1.7s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | 300ms | S |
| CogVideoX 2BVideo | 720×480 | 300ms/frame | S |
| HunyuanVideoVideo | 720×1280 | 600ms/frame | S |
| ChromaImage | 1024×1024 | 300ms | S |
| Z-Image TurboImage | 1536×1536 | 300ms | S |
| Flux.1 DevImage | 1024×1024 | ~1.4s | S |
| Flux.1 SchnellImage | 1024×1024 | 300ms | S |
| LTX Video 13BVideo | 1280×720 | 600ms/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~1.5s | S |
| AnimateDiff v1.5.3Video | 512×768 | 100ms/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | 400ms/frame | S |
| CogVideoX 5BVideo | 720×480 | 400ms/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | 400ms/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | 200ms | S |
| Flux.1 Fill DevImage | 1024×1024 | ~1.3s | S |
| Mochi 1 PreviewVideo | 848×480 | 500ms/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | 500ms/frame | S |
| Helios 14BVideo | 1280×720 | 600ms/frame | S |
| SkyReels V2 14BVideo | 1280×720 | 600ms/frame | S |
| Wan Video 2.1 14BVideo | 720×1280 | 600ms/frame | S |
| Wan Video 2.2 14BVideo | 720×1280 | 600ms/frame | S |
| Qwen ImageImage | 1024×1024 | 500ms | S |
| Qwen Image EditImage | 1024×1024 | 500ms | S |
| Flux.2 DevImage | 1024×1024 | ~14.2s | S |
| MAGI-1Video | 1280×720 | 700ms/frame | S |
| HunyuanImage 3.0Image | 256×256 | 900ms | D |
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 900 GB/s inter-GPU bandwidth with 8% overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× NVIDIA | 141 GB | 353/374 | 4,800 GB/s |
| 2× NVIDIA | 282 GB | 364/374 | 8,832 GB/s |
| 4× NVIDIA | 564 GB | 373/374 | 17,664 GB/s |
| 8× NVIDIA | 1128 GB | 374/374 | 35,328 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.92× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
アップグレードオプション
8× NVIDIA H200 141GBMulti-GPUUnlocks 21 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 138%.
NVLink gives this scale-out path a cleaner inter-GPU story than PCIe-only builds.
〜$30,000 MSRP
NVIDIA B200 180GB次のステップUnlocks 6 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 22%.
〜$30,000 MSRP
NVIDIA GB200 192GBNVIDIAアップグレードUnlocks 6 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 22%.
〜$60,000 MSRP
Unlocks 10 additional models that do not fit on the current setup.
〜$20,000 MSRP
Unlocks 11 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 14%.
〜$8,000 MSRP
NVIDIA H200 141GB (141 GB VRAM) can run these top models: Qwen 3.5 122B A10B (score: 98/100), Devstral 2 123B Instruct (score: 98/100), Mistral Small 4 119B (score: 97/100). See the full compatibility list above.
NVIDIA H200 141GB has 141 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, NVIDIA H200 141GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on NVIDIA H200 141GB, we recommend Devstral 2 123B Instruct. It achieves 58.4 tokens per second with 152K 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 5 upgrade path(s) from NVIDIA H200 141GB: NVIDIA H200 141GB, NVIDIA B200 180GB. Upgrading would unlock larger models and faster inference speeds.
Yes, NVIDIA H200 141GB with 141 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 H200 141GB (141 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 H200 141GB is excellent for AI image generation. With 141 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 H200 141GB with 141 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 141 GB VRAM on NVIDIA H200 141GB, 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 H200 141GB 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 H200 141GB supports up to 8× GPU scaling via NVLink at 900 GB/s. With 8× GPUs, you get 1128 GB effective memory with a 0.92× 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 H200 141GB multi-GPU inference, providing 900 GB/s interconnect bandwidth with only 8% scaling overhead. PCIe-only setups work but have higher overhead (~25%) due to limited inter-GPU bandwidth.
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