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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 40GB is the PCIe variant of NVIDIA's landmark Ampere datacenter GPU, offering the same 312 TFLOPS FP16 compute as the SXM version but with 40 GB of HBM2e and lower bandwidth at 1,555 GB/s. The PCIe form factor makes it compatible with standard servers without SXM infrastructure, and it is available on cloud providers like Google Cloud (A2) and AWS. A single A100 40GB can run 30B models with Q4 quantization and smaller 13B models near FP16, making it a practical and widely accessible inference option. It lacks the 80 GB of the SXM flagship but is substantially more affordable and available.
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
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.
Cost vs cloud API
Assumes 4 hours/day of active inference at 166 tok/s, NVIDIA A100 40GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
71.7M
Tokens/month at this pace
$227
Monthly local cost
$717
Same tokens on cloud API
$3.16
Local $/1M tokens
Break-even: pays for itself in 11.2 months vs cloud API at this workload. Price reference: $8.0k MSRP.
Chat
SQwen 3.5 35B A3B 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
SQwen 3.6 27B 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, lm-studio.
Agentic Coding
SJust out of reach
High-quality models that need a bit more memory
Image & Video Generation
45 of 52 models can generate images or video on your NVIDIA A100 40GB
| 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 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 | 40 GB | 325/374 | 1,555 GB/s |
| 2× NVIDIA | 80 GB | 350/374 | 2,426 GB/s |
| 4× NVIDIA | 160 GB | 359/374 | 4,852 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 34 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 48%.
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.
~$10,000 MSRP
Unlocks 11 additional models that do not fit on the current setup.
~$2,499 MSRP
Unlocks 13 additional models that do not fit on the current setup.
~$4,999 MSRP
Unlocks 26 additional models that do not fit on the current setup.
~$2,499 MSRP
Unlocks 39 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 70%.
~$8,000 MSRP
Compare with similar
| Needs offload |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~1s per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~4.3s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~5.2s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~800ms/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~2.4s/frame |
Qwen 3.6 27B 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, lm-studio.
Reasoning
SDevstral Small 2 24B Instruct 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 |
| 100ms |
| S |
| PixArt-SigmaImage | 1024×1024 | ~1s | S |
| FramePack I2VVideo | 256×256 | ~3s/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 | 256×256 | ~3s/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 | 256×256 | ~3s/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 | B |
| HunyuanVideo 1.5Video | 256×256 | ~1.5s/frame | B |
| Helios 14BVideo | 256×256 | ~3.1s/frame | D |
| SkyReels V2 14BVideo | 256×256 | ~3.1s/frame | D |
| Wan Video 2.1 14BVideo | 256×256 | ~3.1s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~3.1s/frame | F |
| Qwen ImageImage | 256×256 | ~1.6s | F |
| Qwen Image EditImage | 256×256 | ~1.6s | F |
| Flux.2 DevImage | 256×256 | ~45s | F |
| MAGI-1Video | 256×256 | ~2.2s/frame | F |
| 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.
Buying advice
Excellent choice for local AI
Runs 27 of 50 top models well — a strong all-rounder for local inference.
40.0 GB
VRAM
$10,000
MSRP
$250/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 11 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M1 Max 64GB (64.0 GB unified memory) is the next step up.
