# Llama-3.2-1B-Instruct vs TinyLlama-1.1B-Chat-v1.0 Compared
Source: https://savrn.com/models/compare/llama-3-2-1b-instruct-vs-tinyllama-1-1b-chat-v1-0
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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| Field | [Llama-3.2-1B-Instruct](https://savrn.com/models/llama-3-2-1b-instruct) meta-llama/Llama-3.2-1B-Instruct | [TinyLlama-1.1B-Chat-v1.0](https://savrn.com/models/tinyllama-1-1b-chat-v1-0) TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
| --- | --- | --- |
| Publisher | Meta Llama | TinyLlama |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 1.2B parameters | 1.1B parameters |
| Architecture | LlamaForCausalLM | LlamaForCausalLM |
| Library | transformers | transformers |
| Context length | Not stated | 2,048 tokens |
| Repository size | 5.0 GB | 2.2 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | llama3.2 | apache-2.0 |
| Access | Access requested at publisher | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 3 GB | 2.6 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.7 GB | 0.7 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 9213176726f5 | fe8a4ea1ffed |
| Downloads reported by the hub | 4.6M | 1M |
| Last observed | 2026-10-22 | 2026-10-22 |

An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.

## Other Reported Results

These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.

### Llama-3.2-1B-Instruct

| Benchmark | Conditions | Result | Reported by | Revision | Date |
| --- | --- | --- | --- | --- | --- |
| Idavidrein/gpqa | Task diamondMetric diamondSetup GPQA DiamondComparison conditions not established | 18.6869 | [EvalEval](https://huggingface.co/datasets/evaleval/EEE_datastore/blob/192329fb7d6b15b7b0936a1a58ae862aa7e8ba24/flat/objects/aa/ad/aaad0db3-7c5b-4834-a1a2-d96624bdd003.json) Reported by a third party | Evaluated revision not stated | 2026-04-16 |

## SAVRN's Notes on Llama-3.2-1B-Instruct

1.2B parameters at 3.0 GB in 16-bit memory, 0.7 GB at 4-bit: cheap to run on the one 192 GB MI300X at $1.85 an hour we list as the cheapest setup — but access is gated, the llama3.2 license is Meta's own rather than Apache or MIT, and the page records no context length, so verify that with your own tests before promising anything.

Meta frames the 1B and 3B instruct pair as multilingual dialogue engines — English, German, French, Italian, Portuguese, Hindi, Spanish and Thai are listed — tuned with SFT and RLHF for agentic retrieval and summarization. The single evaluation on the page, GPQA Diamond at 18.6869, sets the quality ceiling. Weights ship as pth plus safetensors across 13 files totalling 5.0 GB; one attached paper covers SpinQuant quantization, worth reading before you run 4-bit.

SAVRN Research

## SAVRN's Notes on TinyLlama-1.1B-Chat-v1.0

1.1B parameters, 2,048 tokens of context, 2.2 GB of weights — 2.6 GB of memory at 16-bit, 1.3 at 8-bit, 0.7 at 4-bit. Any GPU you already rent covers it; even the cheapest MI300X at $1.85 an hour is thousands of times more memory than the 4-bit build needs. The card's pitch is the training run itself: a 1.1B Llama pretrained on 3 trillion tokens in 90 days on 16 A100-40G cards.

Apache 2.0, fully permissive, no gate. But 2,048 tokens is tight for anything beyond short exchanges, and quality at 1.1B is what it is — the page offers head-to-head comparisons against gemma-3-1b-it and Llama-3.2-1B-Instruct, which is the right weight class to shop in. Use TinyLlama where the constraint is absolute size — on-device demos, cheap text classification — and step up a size when you have a GPU budget.

SAVRN Research

## Questions

### Which is larger, Llama-3.2-1B-Instruct or TinyLlama-1.1B-Chat-v1.0?

Llama-3.2-1B-Instruct (1.2B parameters) is larger than TinyLlama-1.1B-Chat-v1.0 (1.1B parameters), by the parameter counts their publishers report.

### Which is cheaper to run, Llama-3.2-1B-Instruct or TinyLlama-1.1B-Chat-v1.0?

At 4-bit, Llama-3.2-1B-Instruct fits on 1x MI300X from $1.85 an hour and TinyLlama-1.1B-Chat-v1.0 on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

### Can I use TinyLlama-1.1B-Chat-v1.0 commercially?

Yes. TinyLlama-1.1B-Chat-v1.0 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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