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 V100 SXM is a Volta-generation datacenter GPU that was the world's most capable AI accelerator when launched in 2017. With 32 GB of HBM2 and 900 GB/s bandwidth via SXM2, it introduced the original Tensor Cores for FP16 matrix acceleration. While now several generations behind current Ampere and Hopper hardware, V100 clusters remain available on cloud providers like AWS (P3) and Azure at competitive pricing, making them a budget-accessible option for fine-tuning smaller models or inference. A V100 32GB can run 13B models at Q4 and 7B models at FP16.
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) | Won’t fit | 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
Volta is NVIDIA's datacenter architecture that introduced the first Tensor Cores for mixed-precision matrix operations. The V100 was the dominant AI training GPU before Ampere, offering 32 GB HBM2 memory and NVLink 2.0.
AI Relevance
First-generation Tensor Cores provide FP16 mixed-precision acceleration. The V100's 32 GB HBM2 and high bandwidth (900 GB/s) still make it viable for running mid-sized models, though its age limits software compatibility with newer frameworks.
Consejo de compra
Excelente opción para IA local
Ejecuta 27 de 50 modelos principales bien — un todoterreno sólido para inferencia local.
32.0 GB
VRAM
$8,999
PVP
$281/GB
Coste por GB de VRAM
Mejores modelos para esta GPU
What will limit you first
Este setup está bastante equilibrado para este modelo.
No hay grandes señales de alerta
Esta recomendación tiene margen de memoria suficiente y una velocidad estimada razonable para la carga de trabajo seleccionada.
Best upgrade itinerary
Desbloquea 11 modelos adicionales que hoy no caben en tu setup.
¿Quieres más margen? MacBook Pro M1 Max 64GB (64.0 GB unified memory) es el siguiente paso.
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, lm-studio.
RAG
SThis model is a direct match for rag. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.
Qwen3-Coder 30B A3B InstructQwen3-VL 30B A3B InstructQwen 3 30B A3BQwen 3.5 27BQwen 3.6 35B A3B
Magistral Small 2507Qwen 3.5 35B A3BDevstral Small 2 24B InstructQwen 3.6 27BNemotron Cascade 2 30B A3BNemotron 3 Nano 30B
GPT-OSS 20BDevstral Small 1.1Qwen 3 14B
Gemma 4 26B A4B
Phi-4-reasoning-plus 14BQwen 3 32BQwen 3.5 9BQwen 3 8BMinistral 3 14BQwen 3.5 4B
EXAONE 4.0 32BNemotron Nano 8BPhi-4 Mini Reasoning 4B
Jina Embeddings v3Gemma 4 31B
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 111BQwen 2.5 VL 72BGPT-OSS 120BQwen3-Coder-Next
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2Qwen 3 235B A22BQwen3-Coder 480B A35B InstructLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BCasi al alcance
Modelos de alta calidad que necesitan un poco más de memoria
Image & Video Generation
43 of 52 models can generate images or video on your NVIDIA V100 32GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 400ms | S |
| Stable Diffusion 1.5Image | 512×768 | 700ms | S |
| Realistic Vision v5.1Image | 512×768 | 700ms | S |
| DreamShaper 8Image | 512×768 | 700ms | S |
| LCM DreamShaper v7Image | 512×768 | 200ms | S |
| PixArt-SigmaImage | 1024×1024 | ~3s | S |
| FramePack I2VVideo | 256×256 | ~5.5s/frame | S |
| SDXL TurboImage | 512×512 | 400ms | S |
| SDXL LightningImage | 1024×1024 | ~1.1s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~3s | S |
| Playground v2.5Image | 1024×1024 | ~4.5s | S |
| RealVisXL v5.0Image | 1024×1024 | ~3.3s | S |
| DreamShaper XLImage | 1024×1024 | ~3.3s | S |
| Juggernaut XL v9Image | 1024×1024 | ~3.3s | S |
| Animagine XL 3.1Image | 1024×1024 | ~3.3s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~3.3s | S |
| Animagine XL 4.0Image | 1024×1024 | ~3.3s | S |
| Illustrious XLImage | 1024×1024 | ~3.3s | S |
| Wan Video 2.1 1.3BVideo | 480×832 | ~2.2s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~5.2s | S |
| Flux.2 Klein 4BImage | 1024×1024 | 900ms | S |
| LTX Video 2BVideo | 1280×720 | ~2.6s/frame | S |
| KolorsImage | 1024×1024 | ~6s | S |
| Stable CascadeImage | 1024×1024 | ~7.4s | S |
| AuraFlow v0.3Image | 1536×1536 | ~13.4s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~16.4s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~3s | S |
| CogVideoX 2BVideo | 720×480 | ~2.6s/frame | S |
| HunyuanVideoVideo | 256×256 | ~5.5s/frame | S |
| ChromaImage | 1024×1024 | ~3s | S |
| Z-Image TurboImage | 1536×1536 | ~3.1s | S |
| Flux.1 DevImage | 256×256 | ~23.4s | S |
| Flux.1 SchnellImage | 256×256 | ~4.6s | S |
| LTX Video 13BVideo | 256×256 | ~5.5s/frame | S |
| Flux.1 Kontext DevImage | 256×256 | ~26s | S |
| AnimateDiff v1.5.3Video | 512×768 | ~1.4s/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~4.3s/frame | A |
| CogVideoX 5BVideo | 720×480 | ~3.7s/frame | A |
| Wan2.2 TI2V 5BVideo | 832×480 | ~3.7s/frame | A |
| Flux.2 Klein 9BImage | 1024×1024 | ~1.5s | A |
| Flux.1 Fill DevImage | 256×256 | ~22.1s | B |
| Mochi 1 PreviewVideo | 256×256 | ~8.9s/frame | D |
| HunyuanVideo 1.5Video | 256×256 | ~8.5s/frame | D |
| Helios 14BVideo | 256×256 | ~5.6s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~5.6s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~5.6s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~5.6s/frame | F |
| Qwen ImageImage | 256×256 | ~5s | F |
| Qwen Image EditImage | 256×256 | ~5s | F |
| Flux.2 DevImage | 256×256 | ~2m 21s | F |
| MAGI-1Video | 256×256 | ~7s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~8.8s | 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
Opciones de mejora
Desbloquea 11 modelos adicionales que hoy no caben en tu setup.
~$2,499 MSRP
RTX PRO 5000 Blackwell 48GBMejora NVIDIADesbloquea 13 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 21% en los modelos que sí caben.
~$4,999 MSRP
Desbloquea 26 modelos adicionales que hoy no caben en tu setup.
~$2,499 MSRP
Desbloquea 39 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 126% en los modelos que sí caben.
~$8,000 MSRP
NVIDIA V100 32GB (32 GB VRAM) can run these top models: Qwen3-Coder 30B A3B Instruct (score: 100/100), Qwen3-VL 30B A3B Instruct (score: 99/100), Qwen 3 30B A3B (score: 97/100). See the full compatibility list above.
NVIDIA V100 32GB has 32 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, NVIDIA V100 32GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on NVIDIA V100 32GB, we recommend Qwen 3.6 27B. It achieves 27.4 tokens per second with 187K 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 NVIDIA V100 32GB: MacBook Pro M1 Max 64GB, RTX PRO 5000 Blackwell 48GB. Upgrading would unlock larger models and faster inference speeds.
Yes, NVIDIA V100 32GB with 32 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 V100 32GB (32 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 V100 32GB is excellent for AI image generation. With 32 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 V100 32GB with 32 GB of usable memory can run Qwen 3.5 27B at Q4_K_M (~16.5 GB) with ~7 GB headroom for context and runtime. Quality at Q4 is very close to full precision for most tasks. Run it with: ollama run qwen3.5:27b
With 32 GB on NVIDIA V100 32GB, Q4_K_M is the sweet spot for 27B-35B models, Q6_K for 14B models, and Q8_0 for 8B-9B models. The general rule: use the highest quantization that fits with at least 2-3 GB headroom for KV cache and runtime.
NVIDIA V100 32GB 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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