Idefics3 8B Llama3 — Hardware Requirements & GPU Compatibility
Vision
Idefics3-8B-Llama3 is Hugging Face's 8.5-billion-parameter open vision-language chat model, accepting arbitrary interleaved sequences of images and text and answering in text: image captioning, visual question answering, multi-image storytelling, or plain text-only chat. It combines a SigLIP-SO400M vision encoder with a Llama-3.1-8B-Instruct language backbone, tiling each image into 364x364 sub-images encoded as 169 visual tokens apiece, which sharply improves OCR and document-understanding scores over the earlier Idefics2. Its post-training is supervised fine-tuning only, without an RLHF stage, so it can give terse answers unless prompted further. At 8.5 billion parameters, it fits on a single consumer GPU once quantized. Context length is 131,072 tokens, inherited from its Llama 3.1 backbone. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in August 2024, succeeding Idefics1 and Idefics2 in the same open multimodal model family.
130.1K downloads
306 likesSpecifications
- Publisher
- HuggingFaceM4
- Family
- Llama 3
- Parameters
- 8.5B
- Architecture
- Idefics3ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,259
- Release Date
- 2024-08-05
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Idefics3 8B Llama3 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.2 GB | 21.1 GB | 3.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.7 GB | 21.6 GB | 4.13 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.7 GB | 22.6 GB | 5.08 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.6 GB | 23.5 GB | 6.03 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.5 GB | 24.5 GB | 6.98 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 9.0 GB | 25.9 GB | 8.46 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 17.5 GB | 34.4 GB | 16.92 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Idefics3 8B Llama3?
Q4_K_M · 5.7 GBIdefics3 8B Llama3 (Q4_K_M) requires 5.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 22.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Idefics3 8B Llama3?
Q4_K_M · 5.7 GB58 devices with unified memory can run Idefics3 8B Llama3, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Idefics3 8B Llama3 need?
Idefics3 8B Llama3 requires 5.7 GB of VRAM at Q4_K_M, or 17.5 GB at BF16. Full 131K context adds up to 16.9 GB (22.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.5B × 4.8 bits ÷ 8 = 5.1 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.7 GBQ4_K_M + full context22.6 GB- What's the best quantization for Idefics3 8B Llama3?
For Idefics3 8B Llama3, Q4_K_M (5.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.2 GB.
VRAM requirement by quantization
Q2_K4.2 GBQ4_K_M ★5.7 GBQ5_K_M6.6 GBQ6_K7.5 GBQ8_09.0 GBBF1617.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Idefics3 8B Llama3 on a Mac?
Idefics3 8B Llama3 requires at least 4.2 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Idefics3 8B Llama3 locally?
Yes — Idefics3 8B Llama3 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Idefics3 8B Llama3?
At Q4_K_M, Idefics3 8B Llama3 can reach ~850 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~116 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.7 × 0.65 = ~920 tok/s
Estimated speed at Q4_K_M (5.7 GB)
~920 tok/s~116 tok/s~920 tok/s~850 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Idefics3 8B Llama3?
At Q4_K_M, the download is about 5.08 GB. The full-precision BF16 version is 16.92 GB. The smallest option (Q2_K) is 3.60 GB.
- Which GPUs can run Idefics3 8B Llama3?
52 consumer GPUs can run Idefics3 8B Llama3 at Q4_K_M (5.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Idefics3 8B Llama3?
59 devices with unified memory can run Idefics3 8B Llama3 at Q4_K_M (5.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.