TinyLlama 1.1B Chat v0.3 AWQ
TinyLlama 1.1B Chat v0.3 is a 1.1B-parameter chat model quantized to 4-bit AWQ by TheBloke. It uses the ChatML prompt format and fits comfortably in very low VRAM environments. Context is capped at 2048 tokens.
If you have very little VRAM and just need a quick English chat loop for testing or a lightweight embedded use case, this fits the bill. Do not expect reliable reasoning or anything beyond simple exchanges — the parameter count is the hard ceiling here. For German-language users specifically, this model is a poor fit; it has no multilingual capability. Skip it unless your only constraint is raw memory footprint.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.05/10. License (apache-2.0) is explicitly confirmed in the card, and commercial OK is correct. Metadata aligns: 1.1B params, TinyLlama family, Zhang Peiyuan creator, English-only, ChatML prompt — all verifiable. The editorial voice is honest and operator-style, explicitly calling out the German-hub mismatch and the parameter-count ceiling. One nit: useCases includes 'german' which contradicts the row's own honest weakness — that's a minor inconsistency but doesn't invalidate the row since the description corrects it. Practical deployability is well-covered (VRAM, context limit, AWQ tradeoff). Overall just clears the bar.
Flags: - useCases array contains 'german' which directly contradicts the description and weaknesses — should be removed for consistency - Context length of 2048 is asserted in description but not explicitly shown in the README excerpt; worth a config.json check
Overview
TinyLlama 1.1B Chat v0.3 is a 1.1B-parameter chat model quantized to 4-bit AWQ by TheBloke. It uses the ChatML prompt format and fits comfortably in very low VRAM environments. Context is capped at 2048 tokens.
Strengths
- 1.1B params + AWQ 4-bit quantization means extremely low VRAM footprint
- Apache-2.0 license — commercial use is fine
- Fast inference on modest or edge GPU hardware
- Trained on SlimPajama, StarCoderData, and OpenAssistant datasets
Weaknesses
- English-only — no German or multilingual support despite German-hub placement
- 1.1B parameters limits coherence, reasoning, and factual depth noticeably
- 2048-token context window is short by current standards
- AWQ quantization adds a small but real quality penalty over the full-precision base
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 0.6 GB | 1 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of TinyLlama 1.1B Chat v0.3 AWQ.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run TinyLlama 1.1B Chat v0.3 AWQ?
Can I use TinyLlama 1.1B Chat v0.3 AWQ commercially?
What's the context length of TinyLlama 1.1B Chat v0.3 AWQ?
Source: huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify TinyLlama 1.1B Chat v0.3 AWQ runs on your specific hardware before committing money.