HARDWARE FIT CHECK • Qwen 2.5 Coder 32B Instruct + NVIDIA GeForce RTX 3060 (12GB)25.22 GB / 12 GB VRAM

Can I Run Qwen 2.5 Coder 32B Instruct on NVIDIA GeForce RTX 3060 (12GB)?

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 3060 (12GB) (12 GB).

✕ OOM25.22 GB (210%)0 t/s

Model & Target Hardware

interactive
The #1 Open-Source Coding Benchmark Model for 24GB–32GB GPUs
GPU Topology / Quantity:Single GPU / Unified SoC
Qwen 2.5 Coder 32B Instruct on NVIDIA GeForce RTX 3060 (12GB)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM
Required VRAM
25.22 GB
210% of hardware capacity (12 GB)
Est. Decode Speed
0 tok/s
Dense model @ 360 GB/s
Available Headroom
-13.22 GB
Needs +13.22 GB more VRAM
Memory Allocation & Headroom
25.22 GB allocated of 12 GB capacity
210% utilized
Weights: 19.5 GB
KV: (32K): 4.29 GB
Overhead: 1.43 GB
Deficit: -13.22 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 3060 (12GB) (360 GB/s). Prompt prefill / TTFT (~0 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run qwen2.5-coder:32b

Local Hardware Configurations & Pricing

⚠️ Out of VRAM: High-Capacity Hardware Upgrades
High-bandwidth VRAM specs and current market prices
Consumer GPU✓ Fits Model
32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP
Consumer GPU↑ Upgrade
24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP
Multi-GPU Rig✓ Fits Model
48 GB VRAM

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

936 GB/s700W TDP
Consumer GPU
12 GB VRAM

NVIDIA GeForce RTX 3060 (12GB)

Entry-level budget card for 7B–14B Q4 models

360 GB/s170W TDP

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw65 GB4.29 GB72.32 GB (603%)0 t/sOOM
Q8_08.5 bpw34.45 GB4.29 GB40.7 GB (339%)0 t/sOOM
Q6_K6.56 bpw26.65 GB4.29 GB32.62 GB (272%)0 t/sOOM
Q5_K_M5.68 bpw23.07 GB4.29 GB28.92 GB (241%)0 t/sOOM
Q4_K_M4.8 bpw19.5 GB4.29 GB25.22 GB (210%)0 t/sOOM
FP44.25 bpw17.23 GB4.29 GB22.87 GB (191%)0 t/sOOM
IQ3_XS3.3 bpw13.32 GB4.29 GB18.83 GB (157%)0 t/sOOM

Export Full Architecture & Infrastructure Spec

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FAQ & Reference

Frequently Asked Questions: Qwen 2.5 Coder 32B Instruct on NVIDIA GeForce RTX 3060 (12GB)

How much VRAM does Qwen 2.5 Coder 32B Instruct need on NVIDIA GeForce RTX 3060 (12GB)?

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 3060 (12GB)?

Based on NVIDIA GeForce RTX 3060 (12GB)'s 360 GB/s memory bandwidth, expected decode speed is approximately 0 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.