Run Mistral 7B on RTX 4060 (Setup + Performance)

VRAM Check: Mistral 7B on 8 GB

The NVIDIA RTX 4060 ships with 8 GB of GDDR6 VRAM, which requires quantisation to run Mistral 7B but delivers surprisingly strong performance. Here is the sizing on a dedicated GPU server:

At 4-bit quantisation, Mistral 7B fits comfortably with headroom for a reasonable KV cache. You will need to limit context length to around 4K-8K tokens to stay within VRAM bounds. For full VRAM sizing details, see our Mistral VRAM requirements guide.

Setup with vLLM

# Install vLLM
pip install vllm

# Mistral 7B AWQ 4-bit on RTX 4060
python -m vllm.entrypoints.openai.api_server \
  --model TheBloke/Mistral-7B-Instruct-v0.3-AWQ \
  --quantization awq \
  --dtype float16 \
  --max-model-len 4096 \
  --gpu-memory-utilization 0.85 \
  --port 8000

Setting --gpu-memory-utilization 0.85 leaves a safety margin on the 8 GB card. Our vLLM vs Ollama guide covers framework trade-offs.

Setup with Ollama

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull the 4-bit quantised Mistral 7B
ollama run mistral:7b-instruct-v0.3

# Use as API
curl http://localhost:11434/api/generate \
  -d '{"model": "mistral:7b-instruct-v0.3", "prompt": "Explain CUDA cores."}'

Ollama automatically selects appropriate quantisation for your available VRAM, making it the easier option for RTX 4060 deployments.

RTX 4060 Benchmark Results

Tested with 512-token input, 256-token generation. See the tokens-per-second benchmark tool for updated data.

At 62 tok/s generation speed, the RTX 4060 handles single-user chat applications smoothly. With batching limited to 4 users, it still delivers usable throughput. The Ada Lovelace architecture’s improved tensor cores help close the gap with more expensive GPUs.

Getting the Most from 8 GB

• Use Q4_K_M GGUF via Ollama for the smallest footprint while maintaining quality.
• Limit context to 4096 tokens to keep KV cache VRAM predictable. Use summarisation for longer conversations.
• Disable KV cache quantisation only if you have VRAM headroom; it improves quality at the cost of memory.
• Set batch size conservatively: batch 4 is the practical maximum for sustained serving on 8 GB.
• Consider the RTX 4060 Ti (16 GB) if you need more headroom. See our RTX 4060 Ti hosting page.

Estimate your costs with the cost-per-million-tokens calculator. Browse more guides in the model guides section.

Scaling Up

The RTX 4060 is an excellent budget entry point for Mistral 7B. For higher throughput or FP16 precision, upgrade to an RTX 3090 (24 GB). Compare Mistral against other models in our LLaMA 3 vs Mistral 7B and DeepSeek vs Mistral comparisons. Read the best GPU for LLM inference guide for hardware selection.