vLLM Model Guide for RTX 4070 | Generated by AI

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Question: What open-source models can be served with vLLM on an RTX 4070?

Answer:

Your RTX 4070 (12GB VRAM) + vLLM has stricter limits than llama.cpp:

• vLLM needs weights + KV cache fully in GPU
• no GGUF
• prefers AWQ / GPTQ / FP8 / INT4
• practical size: ≤ 8B safe, ≤ 14B borderline, >14B usually no

vLLM supports many architectures including Qwen, Gemma, DeepSeek, Llama, Mistral, etc. (vLLM)

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Best models for vLLM + RTX 4070 (12GB)

🟢 Best (fits comfortably)

These run smoothly:

1. Qwen2.5 7B AWQ (recommended)

Qwen/Qwen2.5-7B-Instruct-AWQ

Why:

• strong reasoning
• low VRAM
• fast in vLLM
• production stable

Run:

vllm serve Qwen/Qwen2.5-7B-Instruct-AWQ \
  --quantization awq

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2. Gemma 2 9B (FP8 / AWQ)

google/gemma-2-9b-it

Run:

vllm serve google/gemma-2-9b-it \
  --dtype float16

or better:

gemma-2-9b-it-awq

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3. DeepSeek 7B

deepseek-ai/deepseek-llm-7b-chat

Lightweight and stable.

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🟡 Borderline (may fit with tuning)

These might fit with:

• small context (4k)
• fp8 kv cache
• gpu utilization 0.9

Qwen 14B AWQ

Qwen/Qwen2.5-14B-Instruct-AWQ

But many users report 12GB GPU not fitting fully in vLLM. (Reddit)

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Mistral 12B

mistralai/Mistral-Small-12B-Instruct

May require:

--max-model-len 4096
--gpu-memory-utilization 0.9

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🔴 Not possible on RTX 4070 (vLLM)

These won’t fit:

• Qwen 27B
• Gemma 27B
• DeepSeek R1 32B
• Llama 3 70B
• Gemma 26B (your model)

Even 27B INT4 can require ~27GB before KV cache. (Reddit)

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My recommendation (best overall)

Use this:

Qwen2.5-7B-Instruct-AWQ

Best balance:

• strong reasoning
• tool calling
• fast
• fits 4070
• works with vLLM batching

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If you tell me your goal

I’ll pick best:

• coding → best model
• agent/tool calling → best
• reasoning → best
• OpenClaw → best
• API server → best

Just tell me.

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