Deploy GPU ↗
HARDWARE FIT CHECK • Qwen 2.5 72B Instruct + NVIDIA GeForce RTX 4090 (24GB GDDR6X)51.27 GB / 24 GB VRAM

Can I Run Qwen 2.5 72B 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 72B Instruct (72.7B) on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (24 GB).

✕ OOM51.27 GB (214%)0 t/s

Target Hardware & Model

interactive
Top general benchmark open weights model for dual RTX 3090 (48GB) setups
Qwen 2.5 72B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM
Required VRAM
51.27 GB
214% of hardware capacity (24 GB)
Est. Decode Speed
0 tok/s
Dense model @ 1008 GB/s
Available Headroom
-27.27 GB
Needs +27.27 GB more VRAM
Memory Allocation & Headroom
51.27 GB allocated of 24 GB capacity
214% utilized
Weights: 43.62 GB
KV: (32K): 5.37 GB
Overhead: 2.28 GB
Deficit: -27.27 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (1008 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).

Local Hardware Configurations & Pricing

⚠️ Out of VRAM: High-Capacity Hardware Upgrades
High-bandwidth VRAM specs and current market prices
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↑ Upgrade
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
$1,550Amazon Prime →
Apple Silicon✓ Fits Model
112 GB VRAM

Apple Mac Studio M4 Max (128GB Unified Memory)

Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context

546 GB/s145W TDP
$3,499Amazon 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 72B Instruct @ 51.27 GB VRAM)

RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)
Launch 1-Click GPU Pod →
Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)
Rent Spot GPU →
Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)
Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw145.4 GB5.37 GB156.61 GB (653%)0 t/sOOM
Q8_08.5 bpw77.06 GB5.37 GB85.88 GB (358%)0 t/sOOM
Q6_K6.56 bpw59.61 GB5.37 GB67.82 GB (283%)0 t/sOOM
Q5_K_M5.68 bpw51.62 GB5.37 GB59.55 GB (248%)0 t/sOOM
Q4_K_M4.8 bpw43.62 GB5.37 GB51.27 GB (214%)0 t/sOOM
FP44.25 bpw38.53 GB5.37 GB46 GB (192%)0 t/sOOM
IQ3_XS3.3 bpw29.81 GB5.37 GB36.97 GB (154%)0 t/sOOM

Export Full Architecture & Infrastructure Spec

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FAQ & Reference

Frequently Asked Questions: Qwen 2.5 72B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)

How much VRAM does Qwen 2.5 72B Instruct need on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 2.5 72B Instruct requires 51.27 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Qwen 2.5 72B 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 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.