Mistral 7B is a canonical target for mid-tier AI hosting. On the RTX 5060 Ti 16GB it is a perfect fit at our dedicated hosting. This guide covers deployment, performance, and when to pick Mistral over Llama at this tier.
Contents
Fit
| Precision | Weights | KV Cache at 32k Context |
|---|---|---|
| FP16 | ~14 GB | Tight – ~2 GB for KV |
| FP8 | ~7 GB | ~9 GB – comfortable |
| AWQ INT4 | ~4 GB | ~12 GB – very comfortable |
FP8 is the production default. AWQ when you need high concurrency at acceptable quality cost.
Deployment
python -m vllm.entrypoints.openai.api_server \
--model neuralmagic/Mistral-7B-Instruct-v0.3-FP8 \
--quantization fp8 \
--max-model-len 32768 \
--gpu-memory-utilization 0.92 \
--enable-prefix-cachingFor AWQ alternative:
--model TheBloke/Mistral-7B-Instruct-v0.3-AWQ --quantization awqPerformance
| Metric | FP8 | AWQ INT4 |
|---|---|---|
| Batch 1 decode | ~110 t/s | ~130 t/s |
| Batch 8 aggregate | ~570 t/s | ~680 t/s |
| Batch 16 aggregate | ~650 t/s | ~900 t/s |
| TTFT 1k prompt | ~160 ms | ~140 ms |
Concurrency
- FP8 at 30+ t/s/user SLA: 12-16 concurrent
- AWQ INT4 at 30+ t/s/user SLA: 20-30 concurrent
- Queue breakeven: 18+ at FP8, 35+ at AWQ
Mistral vs Llama
On the 5060 Ti 16GB, Mistral 7B and Llama 3 8B are close competitors:
- Mistral 7B: slightly faster (fewer parameters), 32k context native, stronger on European languages
- Llama 3 8B: slightly better general reasoning, better instruction following, broader ecosystem
Either is a fine production choice. Pick Mistral for long-context or multilingual, Llama for general-purpose chat. Both ship FP8 checkpoints.
Mistral 7B Production Hosting
Native FP8 on Blackwell 16GB. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: Mistral 7B benchmark, monthly cost, Mistral Nemo 12B.
