Model Compatibility

AI model and GPU compatibility matrix for Clore.ai

Complete guide to which AI models run on which GPUs on CLORE.AI.

Find GPUs with the right VRAM at CLORE.AI Marketplace.

Quick Reference

Multi-GPU 80GB-class rigs are not listed on the Clore.ai marketplace. The biggest boxes listed today are 4× RTX PRO 6000 Blackwell (96GB each, 380GB total) and 8–11× RTX 5090 (32GB each). A100 / H200 / B200 capacity is sold as bare metal on request. Check GPU Pricing & Availability before sizing a deployment.

Language Models (LLM)

Model	Parameters	Min VRAM	Recommended	Quantization
Llama 3.2	1B	2GB	4GB	Q4, Q8, FP16
Llama 3.2	3B	4GB	6GB	Q4, Q8, FP16
Llama 3.1/3	8B	6GB	12GB	Q4, Q8, FP16
Mistral	7B	6GB	12GB	Q4, Q8, FP16
Qwen 2.5	7B	6GB	12GB	Q4, Q8, FP16
Qwen 2.5	14B	12GB	16GB	Q4, Q8
Qwen 2.5	32B	20GB	24GB	Q4, Q8
Llama 3.1	70B	40GB	48GB	Q4, Q8
Qwen 2.5	72B	48GB	80GB	Q4, Q8
Mixtral	8x7B	24GB	48GB	Q4
DeepSeek-V3	671B	320GB+	640GB	FP8
DeepSeek-R1	671B	320GB+	8x H100	FP8, reasoning model
DeepSeek-R1-Distill	32B	20GB	2x A100 / RTX 5090	Q4/Q8

Image Generation Models

Model	Min VRAM	Recommended	Notes
SD 1.5	4GB	8GB	512x512 native
SD 2.1	6GB	8GB	768x768 native
SDXL	8GB	12GB	1024x1024 native
SDXL Turbo	8GB	12GB	1-4 steps
SD3.5 Large (8B)	16GB	24GB	1024x1024, advanced quality
FLUX.1 schnell	12GB	16GB	4 steps, fast
FLUX.1 dev	16GB	24GB	20-50 steps
TRELLIS	16GB	24GB (RTX 4090)	3D generation from images

Video Generation Models

Model	Min VRAM	Recommended	Output
Stable Video Diffusion	16GB	24GB	4 sec, 576x1024
AnimateDiff	12GB	16GB	2-4 sec
LTX-Video	16GB	24GB (RTX 4090/3090)	5 sec, 768x512, very fast
Wan2.1	24GB	40GB	5 sec, 480p-720p
Hunyuan Video	40GB	80GB	5 sec, 720p
OpenSora	24GB	40GB	Variable

Audio Models

Model	Min VRAM	Recommended	Task
Whisper tiny	1GB	2GB	Transcription
Whisper base	1GB	2GB	Transcription
Whisper small	2GB	4GB	Transcription
Whisper medium	4GB	6GB	Transcription
Whisper large-v3	6GB	10GB	Transcription
Bark	8GB	12GB	Text-to-Speech
Stable Audio	8GB	12GB	Music Generation

Vision & Vision-Language Models

Model	Min VRAM	Recommended	Task
Llama 3.2 Vision 11B	12GB	16GB	Image Understanding
Llama 3.2 Vision 90B	48GB	80GB	Image Understanding
LLaVA 7B	8GB	12GB	Visual QA
LLaVA 13B	16GB	24GB	Visual QA
Qwen2.5-VL 7B	16GB	24GB (RTX 4090)	Image/Video/Doc OCR
Qwen2.5-VL 72B	48GB	2x A100 80GB	Maximum VL capability

Fine-tuning & Training Tools

Tool / Method	Min VRAM	Recommended GPU	Task
Unsloth QLoRA 7B	12GB	RTX 3090 24GB	2x faster QLoRA, low VRAM
Unsloth QLoRA 13B	16GB	RTX 4090 24GB	Fast fine-tuning
LoRA (standard)	12GB	RTX 3090	Parameter-efficient fine-tuning
Full fine-tune 7B	40GB	A100 40GB	Maximum quality training

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Detailed Compatibility Tables

LLM by GPU

GPU	Max Model (Q4)	Max Model (Q8)	Max Model (FP16)
RTX 3060 12GB	13B	7B	3B
RTX 3070 8GB	7B	3B	1B
RTX 3080 10GB	7B	7B	3B
RTX 3090 24GB	30B	13B	7B
RTX 4070 Ti 12GB	13B	7B	3B
RTX 4080 16GB	14B	7B	7B
RTX 4090 24GB	30B	13B	7B
RTX 5090 32GB	70B	14B	13B
A100 40GB	70B	30B	14B
A100 80GB	70B	70B	30B
H100 80GB	70B	70B	30B

Image Generation by GPU

GPU	SD 1.5	SDXL	FLUX schnell	FLUX dev
RTX 3060 12GB	✅ 512	✅ 768	⚠️ 512*	❌
RTX 3070 8GB	✅ 512	⚠️ 512	❌	❌
RTX 3080 10GB	✅ 512	✅ 768	⚠️ 512*	❌
RTX 3090 24GB	✅ 768	✅ 1024	✅ 1024	⚠️ 768*
RTX 4070 Ti 12GB	✅ 512	✅ 768	⚠️ 512*	❌
RTX 4080 16GB	✅ 768	✅ 1024	✅ 768	⚠️ 512*
RTX 4090 24GB	✅ 1024	✅ 1024	✅ 1024	✅ 1024
RTX 5090 32GB	✅ 1024	✅ 1024	✅ 1536	✅ 1536
A100 40GB	✅ 1024	✅ 1024	✅ 1024	✅ 1024
A100 80GB	✅ 2048	✅ 2048	✅ 1536	✅ 1536

*With CPU offload or reduced batch size

Video Generation by GPU

GPU	SVD	AnimateDiff	Wan2.1	Hunyuan
RTX 3060 12GB	❌	⚠️ short	❌	❌
RTX 3090 24GB	✅ 2-4s	✅	⚠️ 480p	❌
RTX 4090 24GB	✅ 4s	✅	✅ 480p	⚠️ short
RTX 5090 32GB	✅ 6s	✅	✅ 720p	✅ 5s
A100 40GB	✅ 4s	✅	✅ 720p	✅ 5s
A100 80GB	✅ 8s	✅	✅ 720p	✅ 10s

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Quantization Guide

What is Quantization?

Quantization reduces model precision to fit in less VRAM:

Format	Bits	VRAM Reduction	Quality Loss
FP32	32	Baseline	None
FP16	16	50%	Minimal
BF16	16	50%	Minimal
FP8	8	75%	Small
Q8	8	75%	Small
Q6_K	6	81%	Small
Q5_K_M	5	84%	Moderate
Q4_K_M	4	87%	Moderate
Q3_K_M	3	91%	Noticeable
Q2_K	2	94%	Significant

VRAM Calculator

Formula: VRAM (GB) ≈ Parameters (B) × Bytes per Parameter

Model Size	FP16	Q8	Q4
1B	2 GB	1 GB	0.5 GB
3B	6 GB	3 GB	1.5 GB
7B	14 GB	7 GB	3.5 GB
8B	16 GB	8 GB	4 GB
13B	26 GB	13 GB	6.5 GB
14B	28 GB	14 GB	7 GB
30B	60 GB	30 GB	15 GB
32B	64 GB	32 GB	16 GB
70B	140 GB	70 GB	35 GB
72B	144 GB	72 GB	36 GB

*Add ~20% for KV cache and overhead

Recommended Quantization by Use Case

Use Case	Recommended	Why
Chat/General	Q4_K_M	Good balance of speed and quality
Coding	Q5_K_M+	Better accuracy for code
Creative Writing	Q4_K_M	Speed matters more
Analysis	Q6_K+	Higher precision needed
Production	FP16/BF16	Maximum quality

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Context Length vs VRAM

How Context Affects VRAM

Each model has a context window (max tokens). Longer context = more VRAM:

Model	Default Context	Max Context	VRAM per 1K tokens
Llama 3 8B	8K	128K	~0.3 GB
Llama 3 70B	8K	128K	~0.5 GB
Qwen 2.5 7B	8K	128K	~0.25 GB
Mistral 7B	8K	32K	~0.25 GB
Mixtral 8x7B	32K	32K	~0.4 GB

Context by GPU (Llama 3 8B Q4)

GPU	Comfortable Context	Maximum Context
RTX 3060 12GB	16K	32K
RTX 3090 24GB	64K	96K
RTX 4090 24GB	64K	96K
RTX 5090 32GB	96K	128K
A100 40GB	96K	128K
A100 80GB	128K	128K

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Multi-GPU Configurations

Tensor Parallelism

Split one model across multiple GPUs:

Configuration	Total VRAM	Max Model (FP16)
2x RTX 3090	48GB	30B
2x RTX 4090	48GB	30B
2x RTX 5090	64GB	32B
4x RTX 5090	128GB	70B
2x A100 40GB	80GB	70B
4x A100 40GB	160GB	100B+
8x A100 80GB	640GB	DeepSeek-V3

vLLM Multi-GPU

# 2 GPUs
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 2

# 4 GPUs
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 4

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Specific Model Guides

Llama 3.1 Family

Variant	Parameters	Min GPU	Recommended Setup
Llama 3.2 1B	1B	Any 4GB	RTX 3060
Llama 3.2 3B	3B	Any 6GB	RTX 3060
Llama 3.1 8B	8B	RTX 3060	RTX 3090
Llama 3.1 70B	70B	A100 40GB	2x A100 40GB
Llama 3.1 405B	405B	8x A100 80GB	8x H100

Mistral/Mixtral Family

Variant	Parameters	Min GPU	Recommended Setup
Mistral 7B	7B	RTX 3060	RTX 3090
Mixtral 8x7B	46.7B	RTX 3090	A100 40GB
Mixtral 8x22B	141B	2x A100 80GB	4x A100 80GB

Qwen 2.5 Family

Variant	Parameters	Min GPU	Recommended Setup
Qwen 2.5 0.5B	0.5B	Any 2GB	Any 4GB
Qwen 2.5 1.5B	1.5B	Any 4GB	RTX 3060
Qwen 2.5 3B	3B	Any 6GB	RTX 3060
Qwen 2.5 7B	7B	RTX 3060	RTX 3090
Qwen 2.5 14B	14B	RTX 3090	RTX 4090
Qwen 2.5 32B	32B	RTX 4090	A100 40GB
Qwen 2.5 72B	72B	A100 40GB	A100 80GB

DeepSeek Models

Variant	Parameters	Min GPU	Recommended Setup
DeepSeek-Coder 6.7B	6.7B	RTX 3060	RTX 3090
DeepSeek-Coder 33B	33B	RTX 4090	A100 40GB
DeepSeek-V2-Lite	15.7B	RTX 3090	A100 40GB
DeepSeek-V3	671B	8x A100 80GB	8x H100
DeepSeek-R1	671B	8x A100 80GB	8x H100 (FP8)
DeepSeek-R1-Distill-Qwen-32B	32B	RTX 5090 32GB	2x A100 40GB
DeepSeek-R1-Distill-Qwen-7B	7B	RTX 3090 24GB	RTX 4090

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Troubleshooting

"CUDA out of memory"

1. Reduce quantization: Q8 → Q4

2. Lower context length: Reduce max_tokens

3. Enable CPU offload: --cpu-offload or enable_model_cpu_offload()

4. Use smaller batch: batch_size=1

5. Try different GPU: Need more VRAM

"Model too large"

1. Use quantized version: GGUF Q4 models

2. Use multiple GPUs: Tensor parallelism

3. Offload to CPU: Slower but works

4. Choose smaller model: 7B instead of 13B

"Slow generation"

1. Upgrade GPU: More VRAM = less offloading

2. Use faster quantization: Q4 is faster than Q8

3. Reduce context: Shorter = faster

4. Enable flash attention: --flash-attn

Next Steps

• GPU Comparison Guide - Detailed GPU specs

• Docker Images Catalog - Ready-to-deploy images

• Quickstart Guide - Get started in 5 minutes