AMD logo AMD AMD Radeon R9 380 4GB mieten.

4GB VRAM 190W 2015
AI models

Models that run on this GPU.

GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the AMD AMD Radeon R9 380 4GB.

DeepSeek R1 Distill Qwen 1.5B

by DeepSeek
FP16
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
2B
Context
128K tokens

Whisper Large v3

by OpenAI
FP16
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
2B
Context
30 tokens

Llama 3.2 1B

by Meta AI
FP16
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
1B
Context
128K tokens

Arcee AI: Spotlight

by Arcee Ai
FP16
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
1B
Context
131K tokens

Stable Diffusion 3.5 Medium

by Stability AI
FP8
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
3B

Qwen 2.5 3B

by Alibaba (Qwen Team)
FP8
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
3B
Context
33K tokens

Gemma 2 2B

by Google DeepMind
FP8
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
3B
Context
8K tokens

Mistral: Ministral 3 3B 2512

by Mistral AI
FP8
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
3B
Context
131K tokens

Qwen2.5 3B Instruct

by Alibaba (Qwen Team)
FP8
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
3B
Context
33K tokens

Meta Llama 3.2 3B Instruct

by Meta AI
FP8
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
3B
Context
131K tokens

Arize AI Qwen 2 1.5B Instruct

by Togethercomputer
INT4
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
5B
Context
33K tokens

Qwen 2 (1.5B)

by Alibaba (Qwen Team)
INT4
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
5B
Context
33K tokens

Qwen 2 Instruct (1.5B)

by Alibaba (Qwen Team)
INT4
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
5B
Context
33K tokens

Qwen2.5 1.5B

by Alibaba (Qwen Team)
INT4
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
5B
Context
131K tokens

Qwen2.5 1.5B Instruct

by Alibaba (Qwen Team)
INT4
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
5B
Context
33K tokens

Qwen3 0.6B

by Alibaba (Qwen Team)
INT4
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
6B
Context
41K tokens

Qwen3 0.6B Base

by Alibaba (Qwen Team)
INT4
Required GPUs
1× AMD Radeon R9 380 4GB
Total VRAM
4 GB
Parameters
6B
Context
33K tokens
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 AMD Radeon R9 380 4GB?
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 4GB of VRAM.
What's the VRAM minimum to run a model on the AMD Radeon R9 380 4GB?
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 AMD Radeon R9 380 4GB 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.