ai-gpu
Mistral 7B VRAM Requirements
How much VRAM does Mistral 7B need? At FP16 it fits in 14 GB. At GGUF Q4 it runs on any 6 GB GPU. See exact requirements and GPU recommendations.
Mistral 7B and Mixtral: VRAM Guide
Mistral AI released two landmark models: Mistral 7B (dense) and Mixtral 8x7B (Mixture of Experts). Both punch above their weight in quality.
Mistral 7B VRAM
| Quantization | VRAM | Minimum GPU |
|---|---|---|
| FP16 | ~14 GB | RTX 4080 16GB, RTX 3090 |
| INT8 | ~7 GB | RTX 3070 8GB |
| INT4 / GGUF Q4 | ~3.8 GB | Any 6GB+ GPU |
Mixtral 8x7B VRAM (MoE)
| Quantization | VRAM | Notes |
|---|---|---|
| FP16 | ~90 GB | All 8 experts loaded in VRAM |
| INT4 / GGUF Q4 | ~26 GB | RTX 4090 (tight) or A100 40GB |
Why MoE Uses Less Active Compute
Mixtral routes each token through only 2 of its 8 expert FFN layers. So despite 46.7B parameters, it only computes ~12.9B per token — similar to a 13B dense model in speed, but with 70B+ quality.
All parameters must still fit in VRAM — MoE doesn't reduce memory, only active compute.
Key Terms
Full glossary →VRAM (Video RAM)
Memory on a GPU used to store model weights, activations, and KV cache during LLM inference. VRAM is the primary constraint when running large language models locally.
Quantization
A technique to reduce model memory usage by representing weights in lower precision (INT8, INT4, GGUF-Q4). Quantization trades a small accuracy loss for significant VRAM reduction.
Frequently Asked Questions
How much VRAM does Mistral 7B need?
Mistral 7B at FP16 needs ~14 GB VRAM — fitting comfortably on an RTX 4080 16GB or RTX 3090. At GGUF Q4_K_M it needs only ~3.8 GB, running on any GPU with 6+ GB VRAM including consumer cards like the GTX 1660.
How does Mistral 7B compare to Llama 3 8B?
Mistral 7B was groundbreaking when released (outperforming Llama 2 13B). Llama 3 8B is now the stronger model for most benchmarks. Mistral 7B remains excellent for instruction following and is the base for many fine-tunes. Choose Llama 3 8B for fresh deployments.
What about Mixtral 8x7B VRAM requirements?
Mixtral 8x7B is a Mixture of Experts (MoE) model. It has 46.7B total parameters but only activates ~12.9B per token. VRAM requirement: ~90 GB at FP16 (all experts loaded), ~26 GB at GGUF Q4. It outperforms Llama 2 70B at a fraction of the active compute.
Can Mistral 7B run on Apple Silicon?
Yes. llama.cpp natively supports Apple Metal (M1/M2/M3/M4). Mistral 7B at GGUF Q4 runs on an M1 with 8GB unified memory (using system RAM as GPU memory). Expect 20–40 tokens/sec — much faster than CPU-only on x86.
Related Tools
AI VRAM
Calculate the GPU VRAM required to run any LLM locally or on cloud GPU. Supports all quantization levels — FP32, FP16, INT8, INT4, and GGUF variants.
GPU Cloud Cost
Compare GPU cloud rental costs across RunPod, Lambda Labs, and Vast.ai. Calculate monthly spend for LLM inference, fine-tuning, and ML training workloads.
Llama 3 8B VRAM
How much GPU VRAM do you need to run Meta Llama 3 8B? At FP16 it needs 16 GB. At INT4/GGUF Q4 it fits in just 4–5 GB — runnable on consumer GPUs.
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