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 4080 Super 16GB is the fastest Ada Lovelace consumer GPU for local AI, offering 736 GB/s GDDR6X bandwidth with 52 TFLOPS FP16 and FP8 support. The 16 GB VRAM fits 13B at FP16 and 30B at Q4, and decode speed on those models is fast — 736 GB/s is close to the RTX 3090's bandwidth but with far better compute efficiency from Ada. It's expensive, and users needing more VRAM should look at the RTX 4090 (24GB). But if 16 GB is your ceiling, this is the fastest Ada card available.
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
Ada Lovelace is NVIDIA's fourth-generation RTX architecture, manufactured on TSMC's custom 4N process. It introduces 4th-generation Tensor Cores with FP8 support, 3rd-generation ray tracing cores, and the Shader Execution Reordering (SER) engine for improved workload scheduling.
AI Relevance
FP8 Tensor Core operations provide a significant uplift for quantized LLM inference compared to Ampere's FP16-only Tensor Cores. DLSS 3 Frame Generation demonstrates the architecture's AI processing capabilities.
Kaufberatung
Nutzbar für lokale KI mit Einschränkungen
Kann 11 von 50 Top-Modellen ausführen, hauptsächlich kleinere. Größere Modelle benötigen starke Quantisierung oder passen nicht.
16.0 GB
VRAM
$999
UVP
$62/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 2 additional models that do not fit on the current setup.
Mehr Spielraum gewünscht? MacBook Pro M3 24GB (24.0 GB unified memory) ist die nächste Stufe.
Cost vs cloud API
Assumes 4 hours/day of active inference at 116 tok/s, RTX 4080 Super 16GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
49.9M
Tokens/month at this pace
$32.8
Monthly local cost
$499
Same tokens on cloud API
$0.657
Local $/1M tokens
Break-even: pays for itself in 2.0 months vs cloud API at this workload. Price reference: $999 MSRP.
Chat
SQwen 3.5 9B 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 3.5 9B 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
SQwen 3.5 9B 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
SQwen 3.5 9B 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.
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 A4BFast erreichbar
Hochwertige Modelle, die etwas mehr Speicher benötigen
Image & Video Generation
31 of 52 models can generate images or video on your RTX 4080 Super 16GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | 700ms | S |
| Stable Diffusion 1.5Image | 512×768 | ~1.4s | S |
| Realistic Vision v5.1Image | 512×768 | ~1.4s | S |
| DreamShaper 8Image | 512×768 | ~1.4s | S |
| LCM DreamShaper v7Image | 512×768 | 400ms | S |
| PixArt-SigmaImage | 1024×1024 | ~5.8s | S |
| FramePack I2VVideo | 256×256 | ~10.6s/frame | S |
| SDXL TurboImage | 512×512 | 700ms | S |
| SDXL LightningImage | 1024×1024 | ~2.2s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~5.8s | S |
| Playground v2.5Image | 1024×1024 | ~8.7s | S |
| RealVisXL v5.0Image | 1024×1024 | ~6.5s | S |
| DreamShaper XLImage | 1024×1024 | ~6.5s | S |
| Juggernaut XL v9Image | 1024×1024 | ~6.5s | S |
| Animagine XL 3.1Image | 1024×1024 | ~6.5s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~6.5s | S |
| Animagine XL 4.0Image | 1024×1024 | ~6.5s | S |
| Illustrious XLImage | 1024×1024 | ~6.5s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~4.2s/frame | S |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~30.3s | S |
| Flux.2 Klein 4BImage | 256×256 | ~3.9s | S |
| LTX Video 2BVideo | 256×256 | ~5s/frame | S |
| KolorsImage | 256×256 | ~30.7s | A |
| Stable CascadeImage | 1024×1024 | ~14.4s | B |
| AuraFlow v0.3Image | 256×256 | ~51.3s | B |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~1m 26s | B |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~15.6s | B |
| CogVideoX 2BVideo | 256×256 | ~5s/frame | D |
| HunyuanVideoVideo | 256×256 | ~10.6s/frame | D |
| ChromaImage | 256×256 | ~5.8s | D |
| Z-Image TurboImage | 256×256 | ~11.9s | D |
| Flux.1 DevImage | 256×256 | ~26s | F |
| Flux.1 SchnellImage | 256×256 | ~5.1s | F |
| LTX Video 13BVideo | 256×256 | ~10.6s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~28.9s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~2.6s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~8.3s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~7.2s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~7.2s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~2.9s | F |
| Flux.1 Fill DevImage | 256×256 | ~24.5s | F |
| Mochi 1 PreviewVideo | 256×256 | ~9.5s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~8.9s/frame | F |
| Helios 14BVideo | 256×256 | ~10.9s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~10.9s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~10.9s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~10.9s/frame | F |
| Qwen ImageImage | 256×256 | ~9.7s | F |
| Qwen Image EditImage | 256×256 | ~9.7s | F |
| Flux.2 DevImage | 256×256 | ~4m 33s | F |
| MAGI-1Video | 256×256 | ~13.5s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~17.1s | 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 30% scaling overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|---|---|---|
| 1× RTX | 16 GB | 283/374 | 736 GB/s |
| 2× RTX | 32 GB | 325/374 | 1,030 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.7× per additional GPU.
Upgrade paths
See what you unlock with more powerful hardware
Upgrade-Optionen
2× RTX 4080 Super 16GBMulti-GPUUnlocks 42 additional models that do not fit on the current setup.
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.
ca. $999 MSRP
Unlocks 2 additional models that do not fit on the current setup.
ca. $1,099 MSRP
RTX A4500 20GBNVIDIA-UpgradeUnlocks 14 additional models that do not fit on the current setup.
ca. $2,000 MSRP
Intel Arc Pro B60 24GBBestes Preis-Leistungs-VerhältnisUnlocks 36 additional models that do not fit on the current setup.
ca. $599 MSRP
Unlocks 81 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 105%.
ca. $8,000 MSRP
RTX 4080 Super 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 4080 Super 16GB has 16 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 4080 Super 16GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 4080 Super 16GB, we recommend Qwen 3.5 9B. It achieves 119.6 tokens per second with 58K context window. Qwen 3.5 9B 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 RTX 4080 Super 16GB: RTX 4080 Super 16GB, MacBook Pro M3 24GB. Upgrading would unlock larger models and faster inference speeds.
RTX 4080 Super 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 4080 Super 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 4080 Super 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 4080 Super 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 4080 Super 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 4080 Super 16GB has enough memory for many local LLMs, but bandwidth still matters a lot for real speed. Once a model fits, a faster-memory GPU can feel significantly better than a slower setup with similar capacity.
RTX 4080 Super 16GB supports up to 2× GPU scaling via PCIe. With 2× GPUs, you get 32 GB effective memory with a 0.7× scaling factor per GPU. This enables running models like Qwen3-Coder 30B A3B Instruct and Qwen 3.5 397B A17B that don't fit on a single card.
RTX 4080 Super 16GB uses PCIe for multi-GPU communication, which has approximately 30% scaling overhead. For best multi-GPU performance, consider NVLink-equipped variants.
Usually yes. If you want to run 2-4× RTX 4080 Super 16GB for local AI, the bottleneck often becomes the platform, not the card. Workstation and server boards give you more CPU PCIe lanes, better x16 slot wiring, more spacing between cards, stronger power delivery, and usually more RAM capacity. Consumer x8/x8 layouts can work, but they are a common weak point in multi-GPU builds.
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