Saiga Llama3 8B GGUF
Saiga Llama3 8B is a Russian-language fine-tune of Meta's Llama 3 8B, trained on the Saiga Scored dataset and packaged in GGUF format for llama.cpp. It targets conversational Russian text generation with an 8192-token context window. Multiple quantization levels are available, so you can trade quality for VRAM depending on your hardware.
If you need a Russian chat model that runs locally on a mid-range GPU, Saiga Llama3 8B is a reasonable starting point. The Llama 3 base is solid, and GGUF packaging keeps the barrier low. The hard blocker is the license — non-commercial only, so rule it out for any revenue-generating product. For personal or research use, it's worth a test run before committing to larger options.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.00/10. License is correctly identified as Llama 3 custom (license_name: llama3), and the non-commercial framing is defensible given Meta's restrictions — though technically Llama 3 allows commercial use under 700M MAU, the row's caution is reasonable for a derivative fine-tune with no explicit commercial grant from IlyaGusev. Metadata (8B params, Russian focus, GGUF, Llama 3 base) all matches the card. Description is concrete and operator-voiced, with honest weaknesses including the modest download count and language scope. Best use case is sharp and the verdict gives a clear go/no-go signal. Context length of 8192 aligns with Llama 3 base. Practical deployability is clearly communicated with the multi-quant GGUF angle.
Flags: - Llama 3 license technically permits commercial use under 700M MAU — 'bars many production uses' is slightly overstated but defensible given derivative ambiguity - Context length 8192 not explicitly stated in card excerpt but is Llama 3 base default — verify
Overview
Saiga Llama3 8B is a Russian-language fine-tune of Meta's Llama 3 8B, trained on the Saiga Scored dataset and packaged in GGUF format for llama.cpp. It targets conversational Russian text generation with an 8192-token context window. Multiple quantization levels are available, so you can trade quality for VRAM depending on your hardware.
Strengths
- Llama 3 8B base gives it a strong general foundation before Russian fine-tuning
- Saiga Scored dataset fine-tune targets conversational Russian specifically
- GGUF format with multiple quants — runs on consumer hardware via llama.cpp
- 8192-token context handles most chat and document tasks without truncation
Weaknesses
- Not commercially licensed — Meta Llama 3 custom license bars many production uses
- 8B parameters and 8192 context are modest; newer Russian-capable models push further on both
- Expect degraded output on non-Russian input — this is not a multilingual model
- Low download count (2,779) means limited community feedback on real-world quality
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 | 4.4 GB | 6 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 Saiga Llama3 8B GGUF.
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 Saiga Llama3 8B GGUF?
Can I use Saiga Llama3 8B GGUF commercially?
What's the context length of Saiga Llama3 8B GGUF?
Source: huggingface.co/IlyaGusev/saiga_llama3_8b_gguf
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Saiga Llama3 8B GGUF runs on your specific hardware before committing money.