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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 A30 is a mid-range Ampere datacenter GPU with HBM2e memory, making it unusual among 24 GB accelerators. Its 933 GB/s of HBM bandwidth is roughly 1.5x that of the A10 and significantly better than GDDR6-based alternatives, which translates directly to faster token generation for memory-bandwidth-bound LLM inference. It sits below the A100 in the Ampere datacenter lineup and supports NVLink for multi-GPU configurations. The A30 is a reasonable choice when inference latency matters more than raw compute.
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.
Chat
SQwen 3 14B 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
SCodestral 2 25.08 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
41 of 52 models can generate images or video on your NVIDIA A30 24GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 200ms | S |
| Stable Diffusion 1.5Image | 512×768 | 500ms | S |
| Realistic Vision v5.1Image | 512×768 | 500ms | S |
| DreamShaper 8Image | 512×768 | 500ms | S |
| LCM DreamShaper v7 |
Multi-GPU scaling
Scale out with multiple GPUs for larger models. NVLink provides 200 GB/s inter-GPU bandwidth with 15% overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× NVIDIA | 24 GB | 319/374 | 933 GB/s |
| 2× NVIDIA | 48 GB | 338/374 | 1,586 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.85× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Upgrade options
Unlocks 19 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 18%.
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.
~$5,500 MSRP
Unlocks 1 additional models that do not fit on the current setup.
~$2,499 MSRP
Unlocks 6 additional models that do not fit on the current setup.
~$4,000 MSRP
Unlocks 17 additional models that do not fit on the current setup.
~$1,099 MSRP
Unlocks 45 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 111%.
~$8,000 MSRP
Compare with similar
| Won’t fit |
| Llama 3.1 70B Q4 |
| — |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~1.9s per image |
| Image Gen (Flux) | Runs with offload | Flux.1 Dev FP16 | ~~8.7s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~10.6s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~1.7s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~4.9s/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 should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Reasoning
SQwen 3 14B 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
AGranite 4.1 8B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
| 512×768 |
| 100ms |
| S |
| PixArt-SigmaImage | 1024×1024 | ~1.9s | S |
| FramePack I2VVideo | 256×256 | ~3.5s/frame | S |
| SDXL TurboImage | 512×512 | 200ms | S |
| SDXL LightningImage | 1024×1024 | 700ms | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~1.9s | S |
| Playground v2.5Image | 1024×1024 | ~2.9s | S |
| RealVisXL v5.0Image | 1024×1024 | ~2.2s | S |
| DreamShaper XLImage | 1024×1024 | ~2.2s | S |
| Juggernaut XL v9Image | 1024×1024 | ~2.2s | S |
| Animagine XL 3.1Image | 1024×1024 | ~2.2s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~2.2s | S |
| Animagine XL 4.0Image | 1024×1024 | ~2.2s | S |
| Illustrious XLImage | 1024×1024 | ~2.2s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~1.4s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~3.4s | S |
| Flux.2 Klein 4BImage | 1024×1024 | 600ms | S |
| LTX Video 2BVideo | 768×512 | ~1.7s/frame | S |
| KolorsImage | 1024×1024 | ~3.9s | S |
| Stable CascadeImage | 1024×1024 | ~4.8s | S |
| AuraFlow v0.3Image | 1536×1536 | ~8.7s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~10.6s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~1.9s | S |
| CogVideoX 2BVideo | 720×480 | ~1.7s/frame | A |
| HunyuanVideoVideo | 256×256 | ~3.5s/frame | A |
| ChromaImage | 256×256 | ~3.5s | A |
| Z-Image TurboImage | 1536×1536 | ~2s | B |
| Flux.1 DevImage | 256×256 | ~8.7s | B |
| Flux.1 SchnellImage | 256×256 | ~1.7s | B |
| LTX Video 13BVideo | 256×256 | ~3.5s/frame | B |
| Flux.1 Kontext DevImage | 256×256 | ~9.6s | B |
| AnimateDiff v1.5.3Video | 512×768 | 900ms/frame | B |
| Cosmos Diffusion 7BVideo | 256×256 | ~5.3s/frame | B |
| CogVideoX 5BVideo | 256×256 | ~5.1s/frame | B |
| Wan2.2 TI2V 5BVideo | 256×256 | ~5.1s/frame | B |
| Flux.2 Klein 9BImage | 256×256 | ~1.8s | D |
| Flux.1 Fill DevImage | 256×256 | ~8.2s | D |
| Mochi 1 PreviewVideo | 256×256 | ~3.2s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~3s/frame | F |
| Helios 14BVideo | 256×256 | ~3.6s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~3.6s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~3.6s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~3.6s/frame | F |
| Qwen ImageImage | 256×256 | ~3.2s | F |
| Qwen Image EditImage | 256×256 | ~3.2s | F |
| Flux.2 DevImage | 256×256 | ~1m 31s | F |
| MAGI-1Video | 256×256 | ~4.5s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~5.7s | 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 26 of 50 top models well — a strong all-rounder for local inference.
24.0 GB
VRAM
$5,500
MSRP
$229/GB
Cost per GB VRAM
Best models for this GPU
What will limit you first
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best upgrade itinerary
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Unlocks 1 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M4 Max 36GB (36.0 GB unified memory) is the next step up.
