InternVL3 8B — Hardware Requirements & GPU Compatibility
Vision
InternVL3-8B is OpenGVLab's roughly 7.9-billion-parameter vision-language model, pairing an InternViT-300M vision encoder with a Qwen2.5-7B language backbone in a ViT-MLP-LLM architecture. It handles general image and video understanding and document analysis, extending into tool use, GUI agent tasks, and 3D scene perception beyond typical captioning. It is comfortably runnable on a single mainstream-to-high-end consumer GPU once quantized. Its language backbone supports a 32,768 token context window. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in April 2025 as part of a 1B-to-78B InternVL3 family sharing the same vision encoder. Its key change versus InternVL2.5 is Native Multimodal Pre-Training, which trains vision and language jointly from the start instead of adapting a language-only model afterward.
86.9K downloads
112 likesSpecifications
- Publisher
- OpenGVLab
- Parameters
- 7.9B
- Architecture
- InternVLChatModel
- Release Date
- 2025-04-10
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does InternVL3 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.7 GB | — | 3.38 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.8 GB | — | 3.48 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.3 GB | — | 3.87 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 5.2 GB | — | 4.77 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.2 GB | — | 5.66 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.2 GB | — | 6.55 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.7 GB | — | 7.94 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run InternVL3 8B?
Q4_K_M · 5.2 GBInternVL3 8B (Q4_K_M) requires 5.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run InternVL3 8B?
Q4_K_M · 5.2 GB58 devices with unified memory can run InternVL3 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download InternVL3 8B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does InternVL3 8B need?
InternVL3 8B requires 5.2 GB of VRAM at Q4_K_M, or 17.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7.9B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.2 GB- What's the best quantization for InternVL3 8B?
For InternVL3 8B, Q4_K_M (5.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.4 GB.
VRAM requirement by quantization
IQ2_XXS2.4 GBQ3_K_S3.8 GBQ4_14.9 GBQ4_K_M ★5.2 GBQ5_K_S6.0 GBBF1617.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run InternVL3 8B on a Mac?
InternVL3 8B requires at least 2.4 GB at IQ2_XXS, 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 InternVL3 8B locally?
Yes — InternVL3 8B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is InternVL3 8B?
At Q4_K_M, InternVL3 8B can reach ~916 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~125 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 ÷ 5.2 × 0.65 = ~992 tok/s
Estimated speed at Q4_K_M (5.2 GB)
~992 tok/s~125 tok/s~992 tok/s~916 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of InternVL3 8B?
At Q4_K_M, the download is about 4.77 GB. The full-precision BF16 version is 15.89 GB. The smallest option (IQ2_XXS) is 2.18 GB.
- Which GPUs can run InternVL3 8B?
52 consumer GPUs can run InternVL3 8B at Q4_K_M (5.2 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run InternVL3 8B?
59 devices with unified memory can run InternVL3 8B at Q4_K_M (5.2 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.