Yes, the RTX 5080 runs Mistral 7B in full FP16 with excellent performance. At roughly 14.5GB for model weights plus KV cache, Mistral 7B in FP16 fits within the RTX 5080’s 16GB VRAM with enough headroom for inference at moderate context lengths. This is one of the best mid-range options for running Mistral without any quantisation compromises.
The Short Answer
YES. Mistral 7B in FP16 needs ~14.5GB, fitting within 16GB with modest headroom.
Mistral 7B v0.3 uses a grouped-query attention architecture that is slightly more VRAM-efficient than standard transformers of equivalent parameter count. The model weights in FP16 consume approximately 13.5GB. With KV cache for a 4096-token context, total usage sits around 14.5GB. The RTX 5080 handles this cleanly, though longer contexts (8K+) may push into tight territory. For full precision analysis, see our best GPU for LLM inference guide.
VRAM Analysis
| Configuration | Weights | KV Cache (4K ctx) | Total | RTX 5080 (16GB) |
|---|---|---|---|---|
| Mistral 7B FP16 | ~13.5GB | ~1GB | ~14.5GB | Fits |
| Mistral 7B FP16 (8K ctx) | ~13.5GB | ~2GB | ~15.5GB | Very tight |
| Mistral 7B INT8 | ~7GB | ~1GB | ~8GB | Fits easily |
| Mistral 7B INT4 | ~4GB | ~1GB | ~5GB | Fits easily |
| Mixtral 8x7B FP16 | ~90GB | ~2GB | ~92GB | No |
FP16 at 4096 context is the sweet spot. If you need longer contexts, dropping to INT8 quantisation frees up VRAM while keeping quality close to full precision. The RTX 5080 also handles Mistral 7B INT4 with enormous headroom, leaving room for additional workloads.
Performance Benchmarks
| GPU | Mistral 7B FP16 (tok/s) | Mistral 7B INT4 (tok/s) |
|---|---|---|
| RTX 4060 (8GB) | N/A (OOM) | ~48 |
| RTX 4060 Ti (16GB) | ~32 | ~52 |
| RTX 3090 (24GB) | ~45 | ~62 |
| RTX 5080 (16GB) | ~68 | ~90 |
| RTX 5090 (32GB) | ~95 | ~120 |
The RTX 5080 delivers 68 tokens per second in full FP16, which is more than double the RTX 4060 Ti. This is fast enough for real-time conversational AI, coding assistants, and RAG pipelines. The GDDR7 memory bandwidth makes a substantial difference for memory-bound LLM inference. See full GPU comparisons on our benchmarks page.
Setup Guide
Deploy Mistral 7B in FP16 using vLLM for production-grade serving:
# vLLM with full FP16 precision
vllm serve mistralai/Mistral-7B-Instruct-v0.3 \
--dtype float16 \
--max-model-len 4096 \
--gpu-memory-utilization 0.92 \
--host 0.0.0.0 --port 8000For quick local testing, Ollama works well:
# Ollama with the FP16 variant
ollama run mistral:7b-instruct-fp16Keep --max-model-len at 4096 to stay within VRAM comfortably. If you need 8K context, reduce --gpu-memory-utilization to 0.95 and monitor for OOM errors.
Recommended Alternative
If you need longer context lengths at FP16, the RTX 3090 with 24GB provides comfortable headroom up to 16K tokens. For Mixtral 8x7B, you will need multi-GPU setups or check the RTX 5090 Mixtral guide.
Explore other RTX 5080 workloads including DeepSeek on the 5080, Flux.1 on the 5080, and SDXL on the 5080. For budget options, our cheapest GPU for AI inference guide covers all tiers. Browse servers on our dedicated GPU hosting page.
Deploy This Model Now
Dedicated GPU servers with the VRAM you need. UK datacenter, full root access.
Browse GPU Servers
