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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~116 tok/s · 128K)Macs from 16 GB (Q4_K_M · ~25 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Idefics3 8B Llama3
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve HuggingFaceM4/Idefics3-8B-Llama3 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~105 tok/s
- generation speed
- Cost per 1M output tokens
- $0.32
- per 1M output tokens
How Much VRAM Does Idefics3 8B Llama3 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.2 GB | 8.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~158 tok/s | |
| 4.7 GB | 8.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~140 tok/s | |
| 5.7 GB | 9.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~116 tok/s | |
| 6.6 GB | 10.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~99 tok/s | |
| 7.5 GB | 11.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~87 tok/s | |
| 9.0 GB | 13.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~73 tok/s | |
| 17.5 GB | 21.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~38 tok/s |
Selected Q4_K_M
4-bit medium quantization7.5GBat 16K context
- Weights
- 5.1 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.5 GB free · ~116 tok/s
≥50 tok/s needs ≥434 GB/s · RTX 5090 ~206, RTX 3090 Ti ~116
Which Hardware 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. 151 devices with unified memory can run Idefics3 8B Llama3, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.5 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~88Simulate output on RTX 3080RTX 2080 Ti~71Simulate output on RTX 2080 TiRTX 3070 Ti~70Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.5 GB free · up to 64K ctx
1 GPU
RX 7900 XT~85Simulate output on RX 7900 XT32–40 GB
fits · 24.5 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~206Simulate output on RTX 5090M5 Max36 GB~57Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~24Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Idefics3 8B Llama3
Llama 3 family · 65 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Idefics3 8B Llama3 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Idefics3 8B Llama3. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Idefics3 8B Llama3 locally?
Yes — Idefics3 8B Llama3 can run locally on consumer hardware. 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 ÷ 5.65 × 0.65 = ~920 tok/s
Estimated speed at Q4_K_M (5.7 GB)
Real-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. 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?
151 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.