consumer

租用 Nvidia RTX 3080.

Name: Nvidia Nvidia RTX 3080
Brand: Nvidia
SKU: rtx3080

Ampere 10GB VRAM 320W

AI models

Models that run on this GPU.

GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia RTX 3080.

Renting for inference?

Pair these models with the cheapest provider on the rental table. See rental rates on the Overview tab →

FAQ

AI models on this GPU.

What AI models can I run on a Nvidia RTX 3080?

The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 10GB of VRAM.

What's the VRAM minimum to run a model on the Nvidia RTX 3080?

Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.

How does quantization (FP16 vs FP8 vs INT4) affect what fits?

Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.

Can I fine-tune on the Nvidia RTX 3080 or only do inference?

Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.

Where do these GPU-count recommendations come from?

We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.

租用 Nvidia RTX 3080.

Daily median across providers.

All providers carrying this GPU.

Run these on this GPU.

Frequently asked.

Models that run on this GPU.

Stable Diffusion XL

Llama 3.2 3B

Qwen 3 4B

Gemma 3 4B

Phi-3.5 Mini

Phi-3 Mini

Qwen3 4B Base

Gemma 3 4b it

Qwen3 4B Instruct 2507

Qwen3.5-4B

DeepSeek R1 Distill Qwen 7B

Llama 3.1 8B

Mistral 7B v0.3

Mistral 7B v0.2

Mistral 7B v0.1

Qwen 2.5 7B

LLaVA 7B

Code Llama 7B

DeepSeek Coder 6.7B

OLMo 3 7B

OLMo 7B

Hermes 3 8B

IBM Granite 3.1 8B

IBM Granite Code 8B

IBM: Granite 4.1 8B

Mistral: Ministral 3 8B 2512

Qwen: Qwen3 VL 8B Thinking

Qwen: Qwen3 VL 8B Instruct

ByteDance: UI-TARS 7B

Qwen: Qwen3 8B

AlfredPros: CodeLLaMa 7B Instruct Solidity

Llama Guard 3 8B

AionLabs: Aion-RP 1.0 (8B)

Qwen: Qwen2.5 7B Instruct

Sao10K: Llama 3 8B Lunaris

NousResearch: Hermes 2 Pro - Llama-3 8B

Meta: Llama 3 8B Instruct

Mistral: Mistral 7B Instruct v0.1

Qwen2.5 7B Instruct Turbo

Meta Llama 3 8B Instruct Lite

Nous Hermes 2 Mixtral 8X7B Dpo

nim/meta/llama-3.1-8b-instruct

Cogito V1 Preview Llama 8B

Mixtral 8x7B Instruct V0.1 FP8 Lora

Qwen 2 (7B)

Qwen2.5 7B

Qwen2.5 7B Instruct

Qwen3 1.7B

Qwen3 1.7B Base

Qwen3 8B Base

meta-llama/Llama-2-7b-chat-hf

Qwen3 8B Lora

Meta Llama 3.1 8B Instruct Awq Int4

Meta Llama 3.1 8B Instruct Turbo

Meta Llama 3 8B Instruct

Mistral (7B) Instruct v0.3

Mixtral 8X7b V0.1

nim/mistralai/mixtral-8x7b-instruct-v01

Mixtral-8x7B Instruct v0.1

Meta Llama 3.1 8B

Molmo 7B D 0924

Meta Llama 3 8B Instruct Reference

Qwen3.5-0.8B

L3-8B-Lunaris-v1-Turbo

Meta-Llama-3.1-8B-Instruct

DeepSeek R1 Distill Qwen 14B

Qwen 2.5 14B

Qwen 3 14B

Phi-4

Phi-3 Medium

LLaVA 13B

Code Llama 13B

DeepSeek Coder V2 Lite

Baichuan2-13B