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 A100 80GB SXM is the reference standard for large-scale AI infrastructure of its era, built on Ampere with 80 GB of HBM2e memory and NVLink 3.0 for high-bandwidth multi-GPU scaling. Its 2,039 GB/s HBM bandwidth and 312 TFLOPS FP16 (with sparsity) made it the dominant GPU for both LLM training and inference. A single A100 80GB can run 70B models at FP16 without quantization, and A100 clusters power many of the largest deployed LLMs in production. It remains a benchmark for comparison despite newer Hopper and Blackwell generations superseding it.
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 with offload | Wan Video 14B |
Architecture
Ampere is NVIDIA's second-generation RTX architecture, built on Samsung's 8nm process. It introduced 3rd-generation Tensor Cores with support for sparsity-accelerated INT8 operations and improved FP16 throughput over Turing.
AI Relevance
Sparsity-aware Tensor Cores can effectively double throughput for structured sparse workloads. However, the lack of FP8 support means quantized inference is less efficient than Ada Lovelace or Blackwell.
Kaufberatung
Ausgezeichnete Wahl für lokale KI
Führt 36 von 50 Top-Modellen gut aus — ein starker Allrounder für lokale Inferenz.
80.0 GB
VRAM
$15,000
UVP
$188/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 1 additional models that do not fit on the current setup.
Mehr Spielraum gewünscht? Mac Studio M2 Ultra 128GB (128.0 GB unified memory) ist die nächste Stufe.
Cost vs cloud API
Assumes 4 hours/day of active inference at 116 tok/s, NVIDIA A100 80GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
50.0M
Tokens/month at this pace
$339
Monthly local cost
$500
Same tokens on cloud API
$6.78
Local $/1M tokens
Break-even: amortizes in 24.3 months vs cloud API. Price reference: $12.0k MSRP.
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
SQwen3-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.
Qwen3-Coder-NextQwen 2.5 VL 72BQwen 3.6 35B A3BQwen3-Coder 30B A3B InstructQwen 3.5 27BQwen 3.5 35B A3BQwen3-VL 30B A3B InstructQwen 3 32BQwen 3.6 27B
Magistral Small 2507Devstral Small 2 24B InstructQwen 3 30B A3BNemotron 3 Nano 30B
Command A 111B
Gemma 4 31BDevstral Small 1.1Qwen 3.5 9BQwen 3 14BNemotron Cascade 2 30B A3B
Phi-4-reasoning-plus 14B
GPT-OSS 20BQwen 3 8B
EXAONE 4.0 32BQwen 3.5 122B A10BGemma 4 26B A4BQwen 3.5 4BMistral Small 4 119BMinistral 3 14BDevstral 2 123B InstructNemotron Nano 8BPhi-4 Mini Reasoning 4BGPT-OSS 120BPixtral Large 124BLeanstral 119B A6B
Jina Embeddings v3
BGE M3Qwen 3.5 397B A17B
Kimi K2.5Kimi K2.6
DeepSeek V4 ProDeepSeek V4 Flash
GLM-5.1GLM-5DeepSeek V3.2Qwen 3 235B A22BQwen3-Coder 480B A35B InstructDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BFast erreichbar
Hochwertige Modelle, die etwas mehr Speicher benötigen
Image & Video Generation
51 of 52 models can generate images or video on your NVIDIA A100 80GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 100ms | S |
| Stable Diffusion 1.5Image | 512×768 | 200ms | S |
| Realistic Vision v5.1Image | 512×768 | 200ms | S |
| DreamShaper 8Image | 512×768 | 200ms | S |
| LCM DreamShaper v7Image | 512×768 | 100ms | S |
| PixArt-SigmaImage | 1024×1024 | ~1s | S |
| FramePack I2VVideo | 1280×720 | ~1.7s/frame | S |
| SDXL TurboImage | 512×512 | 100ms | S |
| SDXL LightningImage | 1024×1024 | 400ms | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~1s | S |
| Playground v2.5Image | 1024×1024 | ~1.4s | S |
| RealVisXL v5.0Image | 1024×1024 | ~1.1s | S |
| DreamShaper XLImage | 1024×1024 | ~1.1s | S |
| Juggernaut XL v9Image | 1024×1024 | ~1.1s | S |
| Animagine XL 3.1Image | 1024×1024 | ~1.1s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~1.1s | S |
| Animagine XL 4.0Image | 1024×1024 | ~1.1s | S |
| Illustrious XLImage | 1024×1024 | ~1.1s | S |
| Wan Video 2.1 1.3BVideo | 480×832 | 700ms/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~1.7s | S |
| Flux.2 Klein 4BImage | 1024×1024 | 300ms | S |
| LTX Video 2BVideo | 1280×720 | 800ms/frame | S |
| KolorsImage | 1024×1024 | ~1.9s | S |
| Stable CascadeImage | 1024×1024 | ~2.4s | S |
| AuraFlow v0.3Image | 1536×1536 | ~4.3s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~5.2s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~1s | S |
| CogVideoX 2BVideo | 720×480 | 800ms/frame | S |
| HunyuanVideoVideo | 720×1280 | ~1.7s/frame | S |
| ChromaImage | 1024×1024 | ~1s | S |
| Z-Image TurboImage | 1536×1536 | ~1s | S |
| Flux.1 DevImage | 1024×1024 | ~4.3s | S |
| Flux.1 SchnellImage | 1024×1024 | 800ms | S |
| LTX Video 13BVideo | 1280×720 | ~1.7s/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~4.8s | S |
| AnimateDiff v1.5.3Video | 512×768 | 400ms/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~1.4s/frame | S |
| CogVideoX 5BVideo | 720×480 | ~1.2s/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | ~1.2s/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | 500ms | S |
| Flux.1 Fill DevImage | 1024×1024 | ~4s | S |
| Mochi 1 PreviewVideo | 848×480 | ~1.6s/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | ~1.5s/frame | S |
| Helios 14BVideo | 1280×720 | ~1.8s/frame | S |
| SkyReels V2 14BVideo | 1280×720 | ~1.8s/frame | S |
| Wan Video 2.1 14BVideo | 720×1280 | ~1.8s/frame | S |
| Wan Video 2.2 14BVideo | 720×1280 | ~1.8s/frame | S |
| Qwen ImageImage | 1024×1024 | ~1.6s | S |
| Qwen Image EditImage | 1024×1024 | ~1.6s | S |
| Flux.2 DevImage | 1024×1024 | ~45s | S |
| MAGI-1Video | 1280×720 | ~2.2s/frame | A |
| HunyuanImage 3.0Image | 256×256 | ~2.8s | 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.
Multi-GPU scaling
Scale out with multiple GPUs for larger models. NVLink provides 600 GB/s inter-GPU bandwidth with 10% overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× NVIDIA | 80 GB | 350/374 | 2,039 GB/s |
| 2× NVIDIA | 160 GB | 359/374 | 3,670 GB/s |
| 4× NVIDIA | 320 GB | 364/374 | 7,340 GB/s |
| 8× NVIDIA | 640 GB | 373/374 | 14,681 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.9× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Upgrade-Optionen
8× NVIDIA A100 80GBMulti-GPUUnlocks 23 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 114%.
NVLink gives this scale-out path a cleaner inter-GPU story than PCIe-only builds.
ca. $15,000 MSRP
Unlocks 1 additional models that do not fit on the current setup.
ca. $3,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA-UpgradeUnlocks 1 additional models that do not fit on the current setup.
ca. $9,999 MSRP
Unlocks 13 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 43%.
ca. $20,000 MSRP
Unlocks 14 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 60%.
ca. $8,000 MSRP
NVIDIA A100 80GB (80 GB VRAM) can run these top models: Qwen3-Coder-Next (score: 97/100), Qwen 2.5 VL 72B (score: 95/100), Qwen 3.6 35B A3B (score: 93/100). See the full compatibility list above.
NVIDIA A100 80GB has 80 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, NVIDIA A100 80GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on NVIDIA A100 80GB, we recommend Qwen3-Coder-Next. It achieves 115.7 tokens per second with 244K context window. Qwen3-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.
There are 5 upgrade path(s) from NVIDIA A100 80GB: NVIDIA A100 80GB, Mac Studio M2 Ultra 128GB. Upgrading would unlock larger models and faster inference speeds.
Yes, NVIDIA A100 80GB with 80 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 A100 80GB (80 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 A100 80GB is excellent for AI image generation. With 80 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 A100 80GB with 80 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 80 GB VRAM on NVIDIA A100 80GB, 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 A100 80GB 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 A100 80GB supports up to 8× GPU scaling via NVLink at 600 GB/s. With 8× GPUs, you get 640 GB effective memory with a 0.9× 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 A100 80GB multi-GPU inference, providing 600 GB/s interconnect bandwidth with only 10% scaling overhead. PCIe-only setups work but have higher overhead (~25%) due to limited inter-GPU bandwidth.
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