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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Yes, but not reliably by loading the entire model onto a free Colab GPU. A standard 4-bit Transformers load is estimated at about 27 GB for the model, and Hugging Face recommends planning for roughly 30 GB of VRAM. Free Colab does not guarantee a GPU with that capacity. The more practical free-tier approach is mixed quantization with CPU/GPU expert offloading, which can run Mixtral on free Colab but adds setup complexity and transfer overhead.
What you need to know before starting
Mixtral’s size and memory requirements
Mixtral 8x7B has about 47 billion total parameters, with about 13 billion active for a given token, and a 32K-token context. The active-parameter figure does not mean you can load only 13 billion parameters: the model’s experts still need to be stored in memory or moved between CPU and GPU.
The memory figures differ by precision and by what the estimate includes. Hugging Face’s Transformers documentation estimates about 90 GB of GPU RAM for float16 and about 27 GB for a 4-bit model, recommending roughly 30 GB of VRAM for the latter. Mistral’s model page gives approximately 94 GB for bf16 and approximately 13 GB for fp4. Treat Mistral’s fp4 figure as an approximate weight figure, not as a guarantee that a complete Transformers session, with runtime overhead and generation cache, will fit in 13 GB.
What free Colab can and cannot promise
Google says free Colab GPU types and usage limits vary, are not guaranteed, and are unpublished. Free notebooks can run for up to 12 hours depending on availability and usage patterns; idle sessions can also end. Consequently, a notebook that fits on one assigned accelerator may fail to load on another, and a running session is not a dependable long-term host.
Choose a loading method
| Approach | Memory or capacity | Trade-off |
|---|---|---|
| Float16/bfloat16 full load | Hugging Face estimates about 90 GB in float16; Mistral lists about 94 GB in bf16. | Requires a large GPU; not a realistic assumption for a free Colab session. |
| 4-bit Transformers with bitsandbytes | Hugging Face estimates about 27 GB for the model and recommends planning for roughly 30 GB VRAM. Mistral’s separate fp4 estimate is about 13 GB for weights. | Simpler than expert offloading, but still may exceed the assigned free GPU’s memory. |
| Mixed quantization with CPU/GPU expert offloading | The offloading project and accompanying study establish that this approach can run Mixtral on free-tier Colab; they do not establish a guaranteed VRAM requirement for every Colab session. | More setup and CPU-to-GPU transfer overhead; no guaranteed tokens-per-second figure is established. |
Try the straightforward 4-bit load first
1. Start a GPU notebook and check the assigned accelerator
In Colab, create a notebook, then select Runtime > Change runtime type and choose an available GPU accelerator. Before downloading the model, run:
!nvidia-smiCheck the GPU name and reported memory. Do not assume a particular accelerator or VRAM amount; Colab availability varies. If the GPU does not have enough capacity for the approximately 30 GB planning target, go to the offloading method below rather than repeatedly attempting the same full 4-bit load.
2. Install the loading libraries
Colab commonly supplies PyTorch with its runtime. Check that it imports, then install or update Transformers, Accelerate, and bitsandbytes:
import torch
print(torch.__version__)
!pip install -U transformers accelerate bitsandbytesIf you update packages that are already imported and then encounter import or CUDA errors, restart the runtime and run the notebook cells again. For repeatable results, record and pin the package versions that worked in your session. Mixtral support and the documented loading examples date to the Transformers 4.36 era, so a package update can change behavior.
3. Load the instruct model in 4-bit
This example uses the Hugging Face model ID mistralai/Mixtral-8x7B-Instruct-v0.1, 4-bit bitsandbytes quantization, float16 compute, and automatic device placement:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=quantization_config,
device_map="auto",
)The first load downloads model files and may take time. If it fails with an out-of-memory error, the assigned GPU is not sufficient for this loading route; use expert offloading rather than assuming that changing the prompt will solve a model-load failure.
4. Send a chat message and limit generation
Use the model’s chat template to format messages. A modest generation cap limits output length and helps keep the key-value cache bounded; the 32K context is a model capability, not a suggestion to fill the entire context on a constrained session.
messages = [
{"role": "user", "content": "Explain mixture-of-experts models in three sentences."}
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to("cuda")
with torch.inference_mode():
output = model.generate(input_ids, max_new_tokens=128)
new_tokens = output[0][input_ids.shape[-1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))If model placement is split across devices, inspect the assigned placement before changing device handling; device_map="auto" is intended to place model components automatically, but it cannot create GPU memory that is not available.
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Mixtral is a mixture-of-experts model. An offloading implementation can keep experts in CPU memory and move the experts needed for the current computation to the GPU. The documented Mixtral offloading project uses HQQ mixed quantization and per-expert CPU/GPU movement; its accompanying study reports running Mixtral 8x7B on free-tier Colab instances.
This is a separate, more specialized route—not a setting to add to the bitsandbytes example above. Follow the installation and notebook instructions provided by the Mixtral offloading project, and choose its Colab-compatible mixed-quantization workflow. The project and study establish feasibility, not a fixed setup that will work with every future Colab GPU assignment. Moving experts between CPU and GPU also adds overhead, so expect a more involved setup and slower generation than a sufficiently large GPU-resident load; the available evidence does not support a current guaranteed speed figure.
Keep the session’s limits in mind
- Save outputs you need. A Colab runtime is temporary, so copy generated results or other required files to persistent storage before the session ends.
- Keep requests modest. Limit
max_new_tokensand avoid unnecessarily long prompts to reduce generation-time memory use. - Plan for interruptions. Google documents variable availability and limits, idle termination, and free sessions of up to 12 hours depending on availability and usage. Do not treat a free runtime as a persistent deployment.
- Do not equate quantized weights with total session memory. The model, loading framework, and generation cache all use resources; a model-size estimate alone does not guarantee that a notebook will run.
Is Mixtral 8x7B still the right model to start with?
Mixtral 8x7B remains a usable model for experimentation, and its weights are under the Apache 2.0 license. However, Mistral marks it retired as of March 30, 2025, and recommends Mistral Small 4 for new integrations. If your goal is specifically to learn or test Mixtral, the Colab approaches above remain relevant; for a new production integration, check Mistral’s current model and service availability rather than assuming Mixtral remains the recommended option.
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