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 5080 16GB is NVIDIA's second-fastest Blackwell consumer GPU, delivering 960 GB/s of GDDR7 bandwidth and FP4/FP8 Tensor Core support. It achieves approximately 132 tokens/second on 7B models, competitive with the RTX 4090, but with only 16 GB of VRAM — not 24 GB. For users who will stay within the 16 GB VRAM ceiling (up to 30B at Q4), the 5080 provides exceptional speed. Users who need 24 GB should look at the RTX 5090 or RTX 4090 instead.
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) | Won’t fit | 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) | Won't fit | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 |
| Video Short (25f) | Runs natively | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
Architecture
Blackwell is NVIDIA's fifth-generation RTX architecture, built on TSMC's 4NP process. It introduces 5th-generation Tensor Cores with native FP4 precision support, enabling double the inference throughput per watt compared to Ada Lovelace's FP8 operations. Key innovations include the Neural Rendering Pipeline for AI-driven shading and the debut of GDDR7 memory in consumer GPUs.
AI Relevance
FP4 Tensor Cores deliver the highest tokens-per-watt efficiency in any consumer architecture. Native FP4 quantization means models can run at lower precision with minimal quality loss, effectively doubling the effective VRAM for model weights.
Conselho de compra
Utilizável para IA local com limitações
Pode rodar 11 de 50 modelos principais, principalmente os menores. Modelos maiores precisam de quantização forte ou não cabem.
16.0 GB
VRAM
$999
Preço sugerido
$62/GB
Custo por GB de VRAM
Melhores modelos para esta 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 2 additional models that do not fit on the current setup.
Quer mais margem? MacBook Pro M3 24GB (24.0 GB unified memory) é o próximo passo.
Cost vs cloud API
Assumes 4 hours/day of active inference at 122 tok/s, RTX 5080 16GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
52.8M
Tokens/month at this pace
$33.5
Monthly local cost
$528
Same tokens on cloud API
$0.635
Local $/1M tokens
Break-even: pays for itself in 1.9 months vs cloud API at this workload. Price reference: $999 MSRP.
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, 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 fits natively with comfortable headroom. Known channels: huggingface, ollama, 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.
Qwen 3.5 9BQwen 3 8BQwen 3 14B
Phi-4-reasoning-plus 14BQwen 3.5 4BNemotron Nano 8B
Ministral 3 14BPhi-4 Mini Reasoning 4B
GPT-OSS 20B
Jina Embeddings v3
BGE M3Qwen3-Coder 30B A3B InstructQwen 3.5 397B A17BDevstral 2 123B Instruct
Kimi K2.5Kimi K2.6
DeepSeek V4 ProQwen 3.5 27BQwen 3.6 27BQwen 3.5 122B A10BQwen3-VL 30B A3B InstructQwen 3.6 35B A3BDeepSeek V4 FlashQwen 3.5 35B A3BMagistral Small 2507Devstral Small 2 24B InstructQwen 3 32BQwen 3 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72BGPT-OSS 120BNemotron 3 Nano 30BQwen3-Coder-NextDevstral Small 1.1
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2Qwen 3 235B A22BQwen3-Coder 480B A35B InstructNemotron Cascade 2 30B A3B
Gemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671B
EXAONE 4.0 32BGemma 4 26B A4BQuase ao alcance
Modelos de alta qualidade que precisam de um pouco mais de memória
Image & Video Generation
31 of 52 models can generate images or video on your RTX 5080 16GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 900ms | S |
| Stable Diffusion 1.5Image | 512×768 | ~1.7s | S |
| Realistic Vision v5.1Image | 512×768 | ~1.7s | S |
| DreamShaper 8Image | 512×768 | ~1.7s | S |
| LCM DreamShaper v7Image | 512×768 | 500ms | S |
| PixArt-SigmaImage | 1024×1024 | ~6.8s | S |
| FramePack I2VVideo | 256×256 | ~12.6s/frame | S |
| SDXL TurboImage | 512×512 | 900ms | S |
| SDXL LightningImage | 1024×1024 | ~2.6s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~6.8s | S |
| Playground v2.5Image | 1024×1024 | ~10.3s | S |
| RealVisXL v5.0Image | 1024×1024 | ~7.7s | S |
| DreamShaper XLImage | 1024×1024 | ~7.7s | S |
| Juggernaut XL v9Image | 1024×1024 | ~7.7s | S |
| Animagine XL 3.1Image | 1024×1024 | ~7.7s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~7.7s | S |
| Animagine XL 4.0Image | 1024×1024 | ~7.7s | S |
| Illustrious XLImage | 1024×1024 | ~7.7s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~5s/frame | S |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~36s | S |
| Flux.2 Klein 4BImage | 256×256 | ~4.6s | S |
| LTX Video 2BVideo | 256×256 | ~5.9s/frame | S |
| KolorsImage | 256×256 | ~36.3s | A |
| Stable CascadeImage | 1024×1024 | ~17.1s | B |
| AuraFlow v0.3Image | 256×256 | ~1m 1s | B |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~1m 42s | B |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~18.5s | B |
| CogVideoX 2BVideo | 256×256 | ~5.9s/frame | D |
| HunyuanVideoVideo | 256×256 | ~12.6s/frame | D |
| ChromaImage | 256×256 | ~6.8s | D |
| Z-Image TurboImage | 256×256 | ~14.1s | D |
| Flux.1 DevImage | 256×256 | ~30.8s | F |
| Flux.1 SchnellImage | 256×256 | ~6s | F |
| LTX Video 13BVideo | 256×256 | ~12.6s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~34.2s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~3.1s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~9.8s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~8.6s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~8.6s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~3.4s | F |
| Flux.1 Fill DevImage | 256×256 | ~29.1s | F |
| Mochi 1 PreviewVideo | 256×256 | ~11.3s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~10.5s/frame | F |
| Helios 14BVideo | 256×256 | ~12.9s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~12.9s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~12.9s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~12.9s/frame | F |
| Qwen ImageImage | 256×256 | ~11.5s | F |
| Qwen Image EditImage | 256×256 | ~11.5s | F |
| Flux.2 DevImage | 256×256 | ~5m 24s | F |
| MAGI-1Video | 256×256 | ~16.1s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~20.3s | 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
Opções de upgrade
Unlocks 2 additional models that do not fit on the current setup.
~$1,099 MSRP
RTX A4500 20GBUpgrade NVIDIAUnlocks 14 additional models that do not fit on the current setup.
~$2,000 MSRP
Intel Arc Pro B60 24GBMelhor custo-benefícioUnlocks 36 additional models that do not fit on the current setup.
~$599 MSRP
Unlocks 81 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 82%.
~$8,000 MSRP
RTX 5080 16GB (16 GB VRAM) can run these top models: Qwen 3.5 9B (score: 97/100), Qwen 3 8B (score: 95/100), Qwen 3 14B (score: 94/100). See the full compatibility list above.
RTX 5080 16GB has 16 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 5080 16GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 5080 16GB, we recommend Qwen 3.5 9B. It achieves 122.2 tokens per second with 58K 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, ollama, lm-studio.
There are 4 upgrade path(s) from RTX 5080 16GB: MacBook Pro M3 24GB, RTX A4500 20GB. Upgrading would unlock larger models and faster inference speeds.
RTX 5080 16GB can run Flux.1 Dev with sequential offloading or at a lower precision (FP8/NF4). The Schnell variant is faster and fits more easily. For best results, use ComfyUI with model offloading enabled.
RTX 5080 16GB (16 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.
RTX 5080 16GB is good for AI image generation. It handles SDXL and SD 3.5 well, and can run Flux with some optimization. 16 GB of usable memory is sufficient for most image generation workflows at standard resolutions.
Qwen 3.5 27B needs ~16.5 GB at Q4_K_M, which is tight for RTX 5080 16GB with 16 GB. You can run the 9B variant at Q8 (9.6 GB) for excellent quality, or try the 35B-A3B MoE variant at Q4 if it fits your context needs.
With 16 GB on RTX 5080 16GB, use Q8_0 for 8B models (best quality), Q4_K_M for 14B models (good balance), and Q4_K_M with limited context for larger models. Avoid going below Q4 — quality drops sharply at Q2-Q3.
RTX 5080 16GB 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.
Compare with similar
Related guides
