HARDWARE FIT CHECK • Qwen 2.5 Coder 32B Instruct + NVIDIA GeForce RTX 3090 (24GB GDDR6X)25.22 GB / 24 GB VRAM

Can I Run Qwen 2.5 Coder 32B Instruct on NVIDIA GeForce RTX 3090 (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 3090 (24GB GDDR6X) (24 GB).

! PARTIAL OFFLOAD25.22 GB (105%)~7 t/s

Qwen 2.5 Coder 32B Instruct on NVIDIA GeForce RTX 3090 (24GB GDDR6X)

! Partial Offload — Slow CPU/RAM Spillover

! PARTIAL OFFLOAD

Required VRAM

25.22 GB

105% of hardware capacity (24 GB)

Est. Decode Speed

~7 tok/s

Dense model @ 936 GB/s

Available Headroom

-1.22 GB

Needs +1.22 GB more VRAM

Memory Allocation & Headroom

25.22 GB allocated of 24 GB capacity

105% utilized

Weights: 19.5 GB

KV: (32K): 4.29 GB

Overhead: 1.43 GB

Deficit: -1.22 GB

Architecture & Throughput Transparency: Single-user decode speed (~7 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 3090 (24GB GDDR6X) (936 GB/s). Prompt prefill / TTFT (~102 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

Local Hardware Configurations & Pricing

Partial Offload: Dedicated 100% GPU Options

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

$1,999Amazon Prime →

Consumer GPU

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

$1,699Amazon Prime →

Multi-GPU Rig✓ Fits Model

48 GB VRAM

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

1648 GB/s700W TDP

$1,550Amazon Prime →

Consumer GPU

24 GB VRAM

NVIDIA GeForce RTX 3090 (24GB GDDR6X)

The #1 budget 24GB card for local LLMs, running 14B–32B models and Q4 70B quantized

936 GB/s350W TDP

$1,499Amazon Prime →

Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin 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)

RunPod Cloud GPUsBest 1-Click Ollama & vLLM

$0.38/hr (RTX 4090 Spot) – $2.39/hr (H100)

Launch 1-Click GPU Pod →

Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090

$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)

Rent Spot GPU →

Lambda GPU CloudEnterprise 8x H100 Clusters

$2.49/hr (H100) – $19.92/hr (8x H100)

Check H100 Availability →

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%)	~33 t/s	FITS
IQ3_XS3.3 bpw	13.32 GB	4.29 GB	18.83 GB (78%)	~42 t/s	FITS

FAQ & Reference

Frequently Asked Questions: Qwen 2.5 Coder 32B Instruct on NVIDIA GeForce RTX 3090 (24GB GDDR6X)

How much VRAM does Qwen 2.5 Coder 32B Instruct need on NVIDIA GeForce RTX 3090 (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 3090 (24GB GDDR6X)?

Based on NVIDIA GeForce RTX 3090 (24GB GDDR6X)'s 936 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.