InternVL2-2B The memory it needs, what you may do with it, and the hardware it suits.
Short answer. InternVL2-2B needs about 3.5 GB of memory at 4-bit, 4.6 GB at 8-bit and 6.7 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 2.2 billion parameters and a 32,768-token context window. Licence in short: commercial use allowed. By memory alone, on ANVE’s launch configurations: ANVE One fits at BF16, ANVE Office fits at BF16, ANVE Office Pro (GPU) fits at BF16. These are estimates from its published size, not benchmarks.
01
What it is
InternVL2-2B is a vision-language model: it reads images (scanned pages, photos, charts and screenshots) as well as text, published by OpenGVLab (Shanghai AI Laboratory) on Hugging Face on 27 June 2024. It has about 2.2 billion parameters and a configured context window of 32,768 tokens.
Its card lists the licence as MIT. In short, the licence allows commercial use, modification and redistribution as long as the copyright notice is kept.
Hugging Face counted about 581,672 downloads of it in the 30 days before we checked (7 Oct 2026), and 83 people have liked it there.
It is built on OpenGVLab/InternViT-300M-448px, according to its card.
02
How much memory it needs
We size it the way you would size any model: the weights (parameters × bits ÷ 8), the KV cache for 8,192 tokens of context, and roughly 1.5 GB for the runtime itself. Real use varies with settings.
Each token of context adds about 0.10 MB of KV cache at 16-bit, so 32,768 tokens would add about 3.2 GB.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
4.4 GB
0.81 GB
6.7 GB
8-bit (Q8_0)
2.3 GB
0.81 GB
4.6 GB
4-bit (Q4_K)
1.2 GB
0.81 GB
3.5 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
InternVLChatModel
Model type
internvl_chat
Layers
24
Hidden size
2,048
Attention heads
16
Key-value heads
8 (grouped-query attention)
Head size
128
Vocabulary
92,553
Configured context
32,768
Stored precision
bfloat16
04
Will it fit on ANVE?
These verdicts are arithmetic, not tests, against the specification at launch: ANVE One can give a model about three quarters of its 64 GB of unified memory, while ANVE Office and ANVE Office Pro hold it on a 24 GB graphics card and keep the rest of the stack in 64 GB or 128 GB of system memory. ANVE’s tested default is Qwen 3.8 27B; this model is not yet validated on ANVE.
Configuration (at launch)
Memory for the model
Verdict
ANVE One
about 48 GB of 64 GB unified memory
fits at BF16
ANVE Office
24 GB on the RTX PRO 4000 Blackwell SFF Edition
fits at BF16
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
fits at BF16
05
What an office would use it for
This size suits quick drafting, classification, extraction and chat over short documents on modest hardware. ANVE pairs a fast 4B-class model with its main model for quick jobs.
06
Variants with the same size
InternVL2_5-2B: a variant with the same size and memory needs (same parameter count, so the memory figures above apply).
07
Similar models to compare
Qwen2-VL-2B-Instruct (2.2B parameters, about 2.7 GB at 4-bit before context; commercial use allowed)
SmolVLM2-2.2B-Instruct (2.2B parameters, about 2.8 GB at 4-bit before context; commercial use allowed)
Qwen3-VL-2B-Instruct (2.1B parameters, about 2.7 GB at 4-bit before context; commercial use allowed)
08
Questions people ask
How much memory does InternVL2-2B need?
About 3.5 GB at 4-bit, 4.6 GB at 8-bit and 6.7 GB at full 16-bit precision, including an 8,192-token KV cache and about 1.5 GB of runtime overhead. These are estimates from its parameter count.
Can InternVL2-2B run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is InternVL2-2B free for commercial use?
Its card lists MIT, which allows commercial use, modification and redistribution as long as the copyright notice is kept. Read the licence itself before relying on this summary; it is not legal advice.
What is InternVL2-2B’s context window?
Its configuration sets 32,768 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.
09
What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.
