Can I Run Qwen 2.5 Coder 32B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 2.5 Coder 32B Instruct (32.5B) on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (24 GB).
⚠️ Partial Offload — Slow CPU/RAM Spillover
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run qwen2.5-coder:32bLocal Hardware Configurations & Pricing
⚡ Partial Offload: Dedicated 100% GPU OptionsNVIDIA 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 70B Q4_K_M & Qwen 3.8 27B Q8
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8B–14B coding models with FP4/Q6_K precision
⚠️ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Qwen 2.5 Coder 32B Instruct @ 25.22 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 65 GB | 4.29 GB | 72.32 GB (301%) | 0 t/s | OOM |
| Q8_08.5 bpw | 34.45 GB | 4.29 GB | 40.7 GB (170%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 26.65 GB | 4.29 GB | 32.62 GB (136%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 23.07 GB | 4.29 GB | 28.92 GB (121%) | ~6 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 19.5 GB | 4.29 GB | 25.22 GB (105%) | ~7 t/s | PARTIAL |
| FP44.25 bpw | 17.23 GB | 4.29 GB | 22.87 GB (95%) | ~38 t/s | FITS |
| IQ3_XS3.3 bpw | 13.32 GB | 4.29 GB | 18.83 GB (78%) | ~48 t/s | FITS |
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Frequently Asked Questions: Qwen 2.5 Coder 32B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)
How much VRAM does Qwen 2.5 Coder 32B Instruct need on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 2.5 Coder 32B Instruct requires 25.22 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Qwen 2.5 Coder 32B Instruct achieve on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
Based on NVIDIA GeForce RTX 4090 (24GB GDDR6X)'s 1008 GB/s memory bandwidth, expected decode speed is approximately 7 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.