Mistral-7B-Instruct-v0.2
Q4 GGUF download: TheBloke/Mistral-7B-Instruct-v0.2-GGUF - 4.37 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Arena rating (LMArena)
Human preference wins from head-to-head chat battles
| Metric | Value |
|---|---|
| Overall arena rating | 1089.5 |
| Rank (overall) | #373 |
| Battle votes | 19,402 |
Overall rating published by LMArena on 2026-10-15 - a pairwise human-preference Elo across live chat battles; higher is better. Fetched 2026-10-19. Open-weights models compete head-to-head with proprietary ones here — no benchmark prompts, just what people pick.
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
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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