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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 RTX A5000 is a high-end Ampere workstation GPU with 24 GB of ECC GDDR6 at 768 GB/s bandwidth — matching the RTX A6000 in bandwidth while offering half the VRAM at a lower price. It handles 30B quantized inference well and can attempt 70B models at very aggressive quantization, making it one of the more capable single-GPU workstation options from the Ampere generation. At $2,500 MSRP it costs roughly the same as the consumer RTX 3090 for identical VRAM plus ECC and professional driver support.
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) | Won’t fit | Llama 3.1 70B Q4 | — |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~6.5s per image |
| Image Gen (Flux) | Runs with offload | Flux.1 Dev FP16 | ~~29.1s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~35.6s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~5.6s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~16.6s/frame |
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.
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
$2,500
MSRP
$104/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.
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 should run, but memory headroom will be limited. Known channels: huggingface, ollama, 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 should run, but memory headroom will be limited. 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, ollama, lm-studio.
RAG
AThis model is a direct match for rag. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.
Just 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 RTX A5000 24GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 800ms | S |
| Stable Diffusion 1.5Image | 512×768 | ~1.6s | S |
| Realistic Vision v5.1Image | 512×768 | ~1.6s | S |
| DreamShaper 8Image | 512×768 | ~1.6s | S |
| LCM DreamShaper v7Image | 512×768 | 500ms | S |
| PixArt-SigmaImage | 1024×1024 | ~6.5s | S |
| FramePack I2VVideo | 256×256 | ~11.9s/frame | S |
| SDXL TurboImage | 512×512 | 800ms | S |
| SDXL LightningImage | 1024×1024 | ~2.4s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~6.5s | S |
| Playground v2.5Image | 1024×1024 | ~9.7s | S |
| RealVisXL v5.0Image | 1024×1024 | ~7.3s | S |
| DreamShaper XLImage | 1024×1024 | ~7.3s | S |
| Juggernaut XL v9Image | 1024×1024 | ~7.3s | S |
| Animagine XL 3.1Image | 1024×1024 | ~7.3s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~7.3s | S |
| Animagine XL 4.0Image | 1024×1024 | ~7.3s | S |
| Illustrious XLImage | 1024×1024 | ~7.3s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~4.7s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~11.3s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~1.9s | S |
| LTX Video 2BVideo | 768×512 | ~5.6s/frame | S |
| KolorsImage | 1024×1024 | ~12.9s | S |
| Stable CascadeImage | 1024×1024 | ~16.2s | S |
| AuraFlow v0.3Image | 1536×1536 | ~29.1s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~35.6s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~6.5s | S |
| CogVideoX 2BVideo | 720×480 | ~5.6s/frame | A |
| HunyuanVideoVideo | 256×256 | ~11.9s/frame | A |
| ChromaImage | 256×256 | ~11.9s | A |
| Z-Image TurboImage | 1536×1536 | ~6.7s | B |
| Flux.1 DevImage | 256×256 | ~29.1s | B |
| Flux.1 SchnellImage | 256×256 | ~5.7s | B |
| LTX Video 13BVideo | 256×256 | ~11.9s/frame | B |
| Flux.1 Kontext DevImage | 256×256 | ~32.4s | B |
| AnimateDiff v1.5.3Video | 512×768 | ~3s/frame | B |
| Cosmos Diffusion 7BVideo | 256×256 | ~17.9s/frame | B |
| CogVideoX 5BVideo | 256×256 | ~17s/frame | B |
| Wan2.2 TI2V 5BVideo | 256×256 | ~17s/frame | B |
| Flux.2 Klein 9BImage | 256×256 | ~5.9s | D |
| Flux.1 Fill DevImage | 256×256 | ~27.5s | D |
| Mochi 1 PreviewVideo | 256×256 | ~10.7s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~9.9s/frame | F |
| Helios 14BVideo | 256×256 | ~12.2s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~12.2s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~12.2s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~12.2s/frame | F |
| Qwen ImageImage | 256×256 | ~10.9s | F |
| Qwen Image EditImage | 256×256 | ~10.9s | F |
| Flux.2 DevImage | 256×256 | ~5m 6s | F |
| MAGI-1Video | 256×256 | ~15.2s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~19.2s | 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. PCIe interconnect with 25% scaling overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× RTX | 24 GB | 319/374 | 768 GB/s |
| 2× RTX | 48 GB | 338/374 | 1,152 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.75× 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 12%.
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.
~$2,500 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 135%.
~$8,000 MSRP
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