How much memory does SmolLM2-135M-Instruct need?

About 1.8 GB at 4-bit, 1.8 GB at 8-bit and 2.0 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 SmolLM2-135M-Instruct run on a 24 GB graphics card?

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

What is SmolLM2-135M-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.

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ANVE

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

SmolLM2-135M-Instruct
Memory at each precision, the licence in plain words, and where it fits.

Short answer. SmolLM2-135M-Instruct needs about 1.8 GB of memory at 4-bit, 1.8 GB at 8-bit and 2.0 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 135 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

SmolLM2-135M-Instruct is a language model for chat, drafting and question answering, published by Hugging Face on Hugging Face on 31 October 2024. It has about 135 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 1,756,510 downloads of it in the 30 days before we checked (7 Oct 2026), and 432 people have liked it there.

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

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 0.02 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)	0.27 GB	0.19 GB	2.0 GB
8-bit (Q8_0)	0.14 GB	0.19 GB	1.8 GB
4-bit (Q4_K)	0.08 GB	0.19 GB	1.8 GB

Architecture at a glance

Setting	Value in config.json
Architecture class	LlamaForCausalLM
Model type	llama
Layers	30
Hidden size	576
Attention heads	9
Key-value heads	3 (grouped-query attention)
Head size	64
Vocabulary	49,152
Configured context	8,192
Stored precision	bfloat16

Will it fit on ANVE?

How the verdicts are worked out: on ANVE One, about three quarters of 64 GB of unified memory is available to the model; on ANVE Office and ANVE Office Pro, the model has to fit in the 24 GB on the graphics card, with 64 GB or 128 GB of system memory for everything else. This is arithmetic against the launch specification, not a test. 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

SmolLM-135M-Instruct: the instruction-tuned version for chat (same parameter count, so the memory figures above apply).

Similar models to compare

LFM2.5-230M (0.2B parameters, about 1.6 GB at 4-bit before context; custom licence: check before commercial use)
gemma-3-270m-it (0.3B parameters, about 1.7 GB at 4-bit before context; commercial use allowed, with conditions)
granite-4.0-350m (0.4B parameters, about 1.7 GB at 4-bit before context; commercial use allowed)

Questions people ask

How much memory does SmolLM2-135M-Instruct need?: About 1.8 GB at 4-bit, 1.8 GB at 8-bit and 2.0 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 SmolLM2-135M-Instruct run on a 24 GB graphics card?: Yes, at BF16 by this estimate, with room left for context.
Is SmolLM2-135M-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 SmolLM2-135M-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.

SmolLM2-135M-InstructMemory at each precision, the licence in plain words, and where it fits.