Can I Run Qwen 2.5 Coder 7B Instruct on NVIDIA RTX 4070 Ti Super (16GB)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 2.5 Coder 7B Instruct (7.6B) on NVIDIA RTX 4070 Ti Super (16GB) (16 GB).
✓ Fits Comfortably in VRAM
Local Hardware Configurations & Pricing
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0
NVIDIA RTX 4070 Ti Super (16GB)
Mid-range 16GB gaming + local AI daily driver
Cloud Instance Options for Qwen 2.5 Coder 7B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 15.2 GB | 0.94 GB | 17.42 GB (109%) | ~6 t/s | PARTIAL |
| Q8_08.5 bpw | 8.06 GB | 0.94 GB | 10.03 GB (63%) | ~54 t/s | FITS |
| Q6_K6.56 bpw | 6.23 GB | 0.94 GB | 8.14 GB (51%) | ~70 t/s | FITS |
| Q5_K_M5.68 bpw | 5.4 GB | 0.94 GB | 7.28 GB (46%) | ~80 t/s | FITS |
| Q4_K_M4.8 bpw | 4.56 GB | 0.94 GB | 6.41 GB (40%) | ~94 t/s | FITS |
| FP44.25 bpw | 4.03 GB | 0.94 GB | 5.86 GB (37%) | ~106 t/s | FITS |
| IQ3_XS3.3 bpw | 3.12 GB | 0.94 GB | 4.92 GB (31%) | ~134 t/s | FITS |
Frequently Asked Questions: Qwen 2.5 Coder 7B Instruct on NVIDIA RTX 4070 Ti Super (16GB)
How much VRAM does Qwen 2.5 Coder 7B Instruct need on NVIDIA RTX 4070 Ti Super (16GB)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 2.5 Coder 7B Instruct requires 6.41 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Qwen 2.5 Coder 7B Instruct achieve on NVIDIA RTX 4070 Ti Super (16GB)?
Based on NVIDIA RTX 4070 Ti Super (16GB)'s 672 GB/s memory bandwidth, expected decode speed is approximately 94 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.