How much memory does SmolVLM-256M-Instruct need?

About 1.8 GB at 4-bit, 2.0 GB at 8-bit and 2.2 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 SmolVLM-256M-Instruct run on a 24 GB graphics card?

Yes, at BF16 by this estimate, with room left for context.

What is SmolVLM-256M-Instruct’s context window?

Its configuration sets 8,192 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.

ANVE: the private AI office, on the record. See what that means

ANVE

Run it on your own hardware · updated 2026-10-16

SmolVLM-256M-Instruct
The memory it needs, what you may do with it, and the hardware it suits.

Short answer. SmolVLM-256M-Instruct needs about 1.8 GB of memory at 4-bit, 2.0 GB at 8-bit and 2.2 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 256 million parameters and an 8,192-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.

What it is

SmolVLM-256M-Instruct is a vision-language model: it reads images (scanned pages, photos, charts and screenshots) as well as text, published by Hugging Face on Hugging Face on 17 January 2025. It has about 256 million parameters and a configured context window of 8,192 tokens.

Its card lists the licence as Apache 2.0. In short, the licence allows commercial use, modification and redistribution, with attribution and a copy of the licence, and includes a patent grant.

Hugging Face counted about 397,613 downloads of it in the 30 days before we checked (7 Oct 2026), and 418 people have liked it there.

It is built on HuggingFaceTB/SmolLM2-135M-Instruct, according to its card.

How much memory it needs

The arithmetic: parameters × bits per weight ÷ 8 gives the weights; add the KV cache, which grows with every token of context; then add about 1.5 GB for the runtime. These are estimates for planning, not measurements.

Each token of context adds about 0.02 MB of KV cache at 16-bit, so 32,768 tokens would add about 0.75 GB.

Precision	Weights	KV cache (8,192 tokens)	Total, estimated
BF16 (full precision)	0.51 GB	0.19 GB	2.2 GB
8-bit (Q8_0)	0.27 GB	0.19 GB	2.0 GB
4-bit (Q4_K)	0.14 GB	0.19 GB	1.8 GB

Architecture at a glance

Setting	Value in config.json
Architecture class	Idefics3ForConditionalGeneration
Model type	idefics3
Layers	30
Hidden size	576
Attention heads	9
Key-value heads	3 (grouped-query attention)
Head size	64
Vocabulary	49,280
Configured context	8,192
Stored precision	bfloat16

Will it fit on ANVE?

Arithmetic against the launch specification, not a test: about three quarters of ANVE One’s 64 GB of unified memory for the model (as AMD’s Variable Graphics Memory allows), and the 24 GB on the graphics card in ANVE Office and ANVE Office Pro, which keep the rest of the stack in 64 GB and 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

What an office would use it for

Models this small are best for routing, classification, autocomplete and simple extraction. They are not reliable for minutes or legal drafting: ANVE never uses anything smaller than a 4B-class model for minutes.

Variants with the same size

SmolVLM2-256M-Video-Instruct: the instruction-tuned version for chat (same parameter count, so the memory figures above apply).

Similar models to compare

LFM2.5-VL-450M (0.4B parameters, about 1.8 GB at 4-bit before context; custom licence: check before commercial use)
Qwen3.5-0.8B (0.9B parameters, about 2.0 GB at 4-bit before context; commercial use allowed)
InternVL2-1B (0.9B parameters, about 2.0 GB at 4-bit before context; commercial use allowed)

Questions people ask

How much memory does SmolVLM-256M-Instruct need?: About 1.8 GB at 4-bit, 2.0 GB at 8-bit and 2.2 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 SmolVLM-256M-Instruct run on a 24 GB graphics card?: Yes, at BF16 by this estimate, with room left for context.
Is SmolVLM-256M-Instruct free for commercial use?: Its card lists Apache 2.0, which allows commercial use, modification and redistribution, with attribution and a copy of the licence, and includes a patent grant. Read the licence itself before relying on this summary; it is not legal advice.
What is SmolVLM-256M-Instruct’s context window?: Its configuration sets 8,192 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.

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.

SmolVLM-256M-InstructThe memory it needs, what you may do with it, and the hardware it suits.