Mistral-7B-Instruct-v0.2
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve mistralai/Mistral-7B-Instruct-v0.2New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Mistral-7B-Instruct-v0.2
Mistral-7B-Instruct-v0.2 is a 7.2B-parameter apache-2.0 model from mistralai. At Q4_K_M it needs roughly 4 GB of VRAM, placing it in the cpu hardware tier.
Specifications
| Spec | Value |
|---|---|
| Parameters | 7.2B |
| Context length | 33K tokens |
| License | apache-2.0 |
| Modalities | text |
| Released | 2023-12-11 |
| Weights | mistralai/Mistral-7B-Instruct-v0.2 |
VRAM requirements
| Quant | VRAM | Runs on |
|---|---|---|
| Q4_K_M | ~4 GB | RTX 4060, RTX 3060 8GB |
| Q5_K_M | ~5 GB | RTX 4060, RTX 3060 8GB |
| Q8_0 | ~8 GB | RTX 4060, RTX 3060 8GB |
| FP16 | ~14 GB | RTX 4060 Ti 16GB, RTX 4080 |
VRAM is estimated from parameter count; MoE models still need all weights resident.
How to run
vLLM:
vllm serve mistralai/Mistral-7B-Instruct-v0.2Popularity
Mistral-7B-Instruct-v0.2 has 2,504,301 downloads in the last month on HuggingFace and 3,157 likes.
Frequently asked
Quick answers to common questions
How much VRAM does Mistral-7B-Instruct-v0.2 need?
Mistral-7B-Instruct-v0.2 with 7.2B parameters needs approximately 4 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Mistral-7B-Instruct-v0.2 better than other mistralai models?
Mistral-7B-Instruct-v0.2 has 7.2B parameters with 32,768 context - a strong choice for general use.
What license is Mistral-7B-Instruct-v0.2 under?
Mistral-7B-Instruct-v0.2 is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs Mistral-7B-Instruct-v0.2 well?
With 7.2B parameters, Mistral-7B-Instruct-v0.2 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Mistral-7B-Instruct-v0.2?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~5 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can Mistral-7B-Instruct-v0.2's context window handle?
Mistral-7B-Instruct-v0.2 supports a 32,768-token context window - enough for most medium-length documents and conversations. Real-world usable context may vary by implementation.
What models compete with Mistral-7B-Instruct-v0.2?
Mistral-7B-Instruct-v0.2 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec
Comments coming soon
Configure NEXT_PUBLIC_GISCUS_REPO_ID and NEXT_PUBLIC_GISCUS_CATEGORY_ID at giscus.app to enable.