Mixtral 8X7B Instruct v0.1 GPTQ
GPTQ 4-bit quantized build of Mistral AI's Mixtral 8x7B Instruct, a sparse mixture-of-experts model with 46.7B total parameters. Natively handles German, French, Italian, Spanish, and English. Apache 2.0 licensed, so commercial use is unrestricted.
If you have a 24 GB GPU and need solid German (plus broader European) instruction following under a clean commercial license, this is one of the more practical options at this weight class. The MoE design gives you better throughput than a dense 46B model would, but the hardware bar is still high. Setup friction is real — budget time for dependency wrangling. Recommend for teams with the hardware already in place; skip if you're working with a single 16 GB card or less.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.15/10. License is explicitly apache-2.0 in the HF metadata and the row reflects that accurately. Params (46.7B MoE), family (mixtral), vendor (Mistral AI), and 8K context are all correct for Mixtral 8x7B Instruct v0.1. The editorial voice is honest and operator-grade — it names the VRAM bar, setup friction, quantization quality loss, and context limitation without sugarcoating. Best use case is reasonably sharp (European multilingual instruction following), though 'business automation' is slightly generic. Practical deployability is well-communicated with concrete VRAM and dependency caveats. Solid row that clears the bar.
Flags: - bestUseCase phrase 'business automation' is mildly generic — could be tightened to a more specific workflow
Overview
GPTQ 4-bit quantized build of Mistral AI's Mixtral 8x7B Instruct, a sparse mixture-of-experts model with 46.7B total parameters. Natively handles German, French, Italian, Spanish, and English. Apache 2.0 licensed, so commercial use is unrestricted.
Strengths
- Native German support alongside four other European languages
- MoE architecture keeps active parameter count well below 46.7B total, improving throughput
- Apache 2.0 — commercial use and modification permitted without restrictions
- GPTQ 4-bit reduces VRAM footprint relative to full-precision weights
Weaknesses
- Still needs ~24 GB VRAM at 4-bit — a single consumer GPU won't cut it for most setups
- Requires Transformers ≥4.36 and AutoGPTQ or a dev Transformers build; setup is non-trivial
- 4-bit quantization introduces measurable quality loss versus the full-precision model
- Only 8192-token context — smaller than several newer competitors
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 | 25.7 GB | 33 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 Mixtral 8X7B Instruct v0.1 GPTQ.
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 Mixtral 8X7B Instruct v0.1 GPTQ?
Can I use Mixtral 8X7B Instruct v0.1 GPTQ commercially?
What's the context length of Mixtral 8X7B Instruct v0.1 GPTQ?
Source: huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Mixtral 8X7B Instruct v0.1 GPTQ runs on your specific hardware before committing money.