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 3080 10GB is a powerhouse Ampere card with exceptional bandwidth (760 GB/s GDDR6X) but frustratingly tight VRAM for AI use. At 10 GB, it can run 7B models comfortably and squeeze a 13B model at Q4, but the 3080 12GB version is almost always the better AI choice when available. The 760 GB/s bandwidth — significantly more than the 12GB variant's 912 GB/s is not far off — means decode speed is excellent for what fits. Avoid if you can get the 12GB version; buy if the price gap is meaningful.
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) | Won't fit | SD 3.5 Large FP16 |
| Video Short (25f) | Very constrained | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
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.
购买建议
有限制地可用于本地 AI
可运行 50 个顶级模型中的 7 个，主要是较小的模型。较大模型需要强量化或无法适配。
10.0 GB
VRAM
$699
建议零售价
$70/GB
每 GB VRAM 成本
最适合此 GPU 的模型
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.
想要更多余量？ GTX 1080 Ti 11GB (11.0 GB VRAM) 是下一步升级选择。
Cost vs cloud API
Assumes 4 hours/day of active inference at 91 tok/s, RTX 3080 10GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
39.4M
Tokens/month at this pace
$19.6
Monthly local cost
$394
Same tokens on cloud API
$0.499
Local $/1M tokens
Break-even: pays for itself in 1.3 months vs cloud API at this workload. Price reference: $500 (used market).
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
AThis 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
AThis model is still usable for agentic-coding, but it is not the most specialized pick. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.
Reasoning
SThis model is a direct match for reasoning. 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.
RAG
AThis model is still usable for rag, but it is not the most specialized pick. 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 3.5 4BNemotron Nano 8B
Phi-4 Mini Reasoning 4B
Jina Embeddings v3
BGE M3Qwen3-Coder 30B A3B InstructQwen 3.5 397B A17B
Devstral 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 14BQwen 3 30B A3BMistral Small 4 119B
Command A 111BQwen 2.5 VL 72B
GPT-OSS 120BNemotron 3 Nano 30BQwen3-Coder-NextPhi-4-reasoning-plus 14BDevstral Small 1.1
GLM-5.1Pixtral Large 124BGLM-5DeepSeek V3.2GPT-OSS 20BQwen 3 235B A22BQwen3-Coder 480B A35B InstructNemotron Cascade 2 30B A3B
Gemma 4 31BLeanstral 119B A6BDeepSeek Coder V2 236BDeepSeek R1 671BDeepSeek V3.1 671BMinistral 3 14B
EXAONE 4.0 32BGemma 4 26B A4B触手可及
高质量模型，只需稍多一点内存
Image & Video Generation
23 of 52 models can generate images or video on your RTX 3080 10GB
| 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 | 256×256 | ~5.5s | S |
| FramePack I2VVideo | 256×256 | ~10s/frame | S |
| SDXL TurboImage | 512×512 | 700ms | S |
| SDXL LightningImage | 1024×1024 | ~2.1s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~5.5s | S |
| Playground v2.5Image | 256×256 | ~19.3s | S |
| RealVisXL v5.0Image | 1024×1024 | ~6.2s | S |
| DreamShaper XLImage | 1024×1024 | ~6.2s | S |
| Juggernaut XL v9Image | 1024×1024 | ~6.2s | S |
| Animagine XL 3.1Image | 1024×1024 | ~6.2s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~6.2s | S |
| Animagine XL 4.0Image | 1024×1024 | ~6.2s | S |
| Illustrious XLImage | 1024×1024 | ~6.2s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~4s/frame | B |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~9.6s | B |
| Flux.2 Klein 4BImage | 256×256 | ~1.6s | B |
| LTX Video 2BVideo | 256×256 | ~4.7s/frame | D |
| KolorsImage | 256×256 | ~10.9s | D |
| Stable CascadeImage | 256×256 | ~31.9s | F |
| AuraFlow v0.3Image | 256×256 | ~24.6s | F |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~30.1s | F |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~5.5s | F |
| CogVideoX 2BVideo | 256×256 | ~4.7s/frame | F |
| HunyuanVideoVideo | 256×256 | ~10s/frame | F |
| ChromaImage | 256×256 | ~5.5s | F |
| Z-Image TurboImage | 256×256 | ~5.6s | F |
| Flux.1 DevImage | 256×256 | ~24.6s | F |
| Flux.1 SchnellImage | 256×256 | ~4.8s | F |
| LTX Video 13BVideo | 256×256 | ~10s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~27.3s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~2.5s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~7.8s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~6.8s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~6.8s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~2.7s | F |
| Flux.1 Fill DevImage | 256×256 | ~23.2s | F |
| Mochi 1 PreviewVideo | 256×256 | ~9s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~8.4s/frame | F |
| Helios 14BVideo | 256×256 | ~10.3s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~10.3s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~10.3s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~10.3s/frame | F |
| Qwen ImageImage | 256×256 | ~9.2s | F |
| Qwen Image EditImage | 256×256 | ~9.2s | F |
| Flux.2 DevImage | 256×256 | ~4m 19s | F |
| MAGI-1Video | 256×256 | ~12.8s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~16.2s | 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.
Upgrade paths
See what you unlock with more powerful hardware
升级选项
GTX 1080 Ti 11GB下一步升级Unlocks 1 additional models that do not fit on the current setup.
~$699 MSRP
RTX 3060 12GBNVIDIA 升级Unlocks 4 additional models that do not fit on the current setup.
~$329 MSRP
Intel Arc Pro B60 24GB最佳性价比Unlocks 77 additional models that do not fit on the current setup.
~$599 MSRP
Unlocks 122 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 149%.
~$8,000 MSRP
RTX 3080 10GB (10 GB VRAM) can run these top models: Qwen 3.5 9B (score: 95/100), Qwen 3 8B (score: 94/100), Qwen 3.5 4B (score: 94/100). See the full compatibility list above.
RTX 3080 10GB has 10 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.
Yes, RTX 3080 10GB is excellent for running LLMs locally with top compatibility scores above 80/100.
For coding on RTX 3080 10GB, we recommend Gemma 4 E4B. It achieves 81.0 tokens per second with 40K 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 3080 10GB: GTX 1080 Ti 11GB, RTX 3060 12GB. Upgrading would unlock larger models and faster inference speeds.
Flux.1 Dev requires around 24 GB of usable memory at FP16. With 10 GB, RTX 3080 10GB cannot run Flux natively. Consider quantized GGUF variants or the smaller Schnell model with aggressive offloading.
RTX 3080 10GB (10 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, video generation is limited by available memory. Check the AI Capability Matrix above for detailed compatibility.
RTX 3080 10GB can handle basic AI image generation with SDXL and SD 1.5. With 10 GB of usable memory, larger models like Flux will need quantization or offloading. Best suited for standard resolution (512-1024px) generation.
Qwen 3.5 27B does not fit on RTX 3080 10GB with 10 GB. However, Qwen 3.5 9B at Q4 (5.5 GB) or Q5 (6.5 GB) runs well on your GPU. The 4B variant fits at Q8 for near-lossless quality.
With 10 GB on RTX 3080 10GB, use Q4_K_M for 8B models and Q4_K_M with tight context for 14B models. Q5_K_M is a good middle ground when the model fits. For the best quality-to-size ratio, Q4_K_M is the most popular choice.
On RTX 3080 10GB, capacity is usually the first gate: if the model does not fit, bandwidth does not matter. But once a model fits, memory bandwidth is what largely determines tokens per second. In practice, you want enough memory to fit the model plus headroom, then as much bandwidth as your budget allows.
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