InternVL2 1B — Hardware Requirements & GPU Compatibility
Vision
InternVL2-1B is OpenGVLab's smallest instruction-tuned vision-language model in the InternVL 2.0 family, combining an InternViT-300M-448px vision encoder with a Qwen2-0.5B-Instruct language model via an MLP projector. It handles document and chart comprehension, OCR, scene-text reading, and general visual question answering, and can take multiple images or video frames as input. At under a billion parameters, it is light enough to run on a CPU or a low-end consumer GPU. InternVL 2.0 models were trained with an 8K context window and improved multi-image and video handling over the earlier InternVL 1.5 generation. Context length is 8,192 tokens. It is released under the MIT license, permitting unrestricted commercial and research use, and was published in July 2024. It was later superseded by the InternVL2.5 series.
695.5K downloads
83 likesSpecifications
- Publisher
- OpenGVLab
- Parameters
- 938M
- Architecture
- InternVLChatModel
- Release Date
- 2024-07-08
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does InternVL2 1B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.4 GB | — | 0.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.5 GB | — | 0.41 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.5 GB | — | 0.46 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.6 GB | — | 0.56 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.7 GB | — | 0.67 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.8 GB | — | 0.77 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.0 GB | — | 0.94 GB | 8-bit quantization, near-lossless |
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 InternVL2 1B?
Q4_K_M · 0.6 GBInternVL2 1B (Q4_K_M) requires 0.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ 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 InternVL2 1B?
Q4_K_M · 0.6 GB59 devices with unified memory can run InternVL2 1B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download InternVL2 1B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does InternVL2 1B need?
InternVL2 1B requires 0.6 GB of VRAM at Q4_K_M, or 2.1 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 938M × 4.8 bits ÷ 8 = 0.6 GB
VRAM usage by quantization
Q4_K_M0.6 GB- What's the best quantization for InternVL2 1B?
For InternVL2 1B, Q4_K_M (0.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.4 GB.
VRAM requirement by quantization
Q2_K0.4 GBQ3_K_L0.5 GBQ4_K_M ★0.6 GBQ5_K_S0.7 GBQ5_K_M0.7 GBBF162.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run InternVL2 1B on a Mac?
InternVL2 1B requires at least 0.4 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 InternVL2 1B locally?
Yes — InternVL2 1B can run locally on consumer hardware. At Q4_K_M quantization it needs 0.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is InternVL2 1B?
At Q4_K_M, InternVL2 1B can reach ~7742 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1057 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 ÷ 0.6 × 0.65 = ~8387 tok/s
Estimated speed at Q4_K_M (0.6 GB)
~8387 tok/s~1057 tok/s~8387 tok/s~7742 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of InternVL2 1B?
At Q4_K_M, the download is about 0.56 GB. The full-precision BF16 version is 1.88 GB. The smallest option (Q2_K) is 0.40 GB.
- Which GPUs can run InternVL2 1B?
52 consumer GPUs can run InternVL2 1B at Q4_K_M (0.6 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 InternVL2 1B?
59 devices with unified memory can run InternVL2 1B at Q4_K_M (0.6 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.