BENCHMARK REPORT
Can I Run Qwen 2.5 72B Instruct on NVIDIA GeForce RTX 3090?
Comprehensive technical verdict based on static weights, dynamic KV Cache (8k baseline context window), and CUDA runtime overhead.
Official Verdict
NO — CUDA OOM
Executive Hardware Compatibility Summary: Qwen 2.5 72B Instruct on NVIDIA GeForce RTX 3090
Authoritative verdict, memory allocation, and token generation performance:
Direct Answer: CUDA OOM — Deficit of 24.9 GBQwen 2.5 72B Instruct exceeds dedicated physical VRAM and triggers PCIe layer offloading on NVIDIA GeForce RTX 3090 (24GB). At standard Q4_K_M 4-bit quantization and an 8,192-token context window, total memory demand is 48.87 GB (44.27 GB weights + 2.65 GB dynamic KV cache + 1.15 GB OS & CUDA buffers). Physical VRAM is exceeded by 24.87 GB, forcing 45 transformer layers to offload across the PCIe bus to host DDR RAM. Estimated generation throughput is ~3 tokens/sec (~333.3 ms per token) with prompt prefill processing reaching ~410 tok/s.
Run Verdict
CUDA OOM (Offload)
Total Required VRAM
48.87 GB / 24 GB
Decoding Speed
~3 tok/s (333.3ms)
Safe Context Window
Offload to RAM
Hardware Architecture & VRAM Topology
AmpereNVIDIA GeForce RTX 3090 • 936 GB/s Bandwidth
Token Generation
~3 tok/s(333.3 ms)
OOM SPILL
VRAM Utilization Breakdown48.87 GB / 24 GB (204%)
VRAM Limit (24G)
44.27G
-24.87G Spill
Weights
44.27 GB
KV Cache
2.65 GB
Activation
0.8 GB
OS/Driver
1.15 GB
Overflow
24.87 GB
Hardware Chokepoint: PCIe Bus Throttling Active
GPU VRAM (24 GB)
35 / 80 Layers Loaded
936 GB/s Bus
PCIe 4.0 x16 Bottleneck
31.5 GB/s
-97% Bandwidth Drop
Host DDR5 System RAM
45 Layers Offloaded
+28.6 GB RAM Siphoned
Why does your generation speed collapse to ~3 tok/s? Because for every single generated token, model weights across the 45 offloaded layers must be transferred across the 31.5 GB/s PCIe slot from host system RAM.
Context Window vs VRAM Scaling Curve
Blue line indicates total VRAM demand across context lengths; Red dashed line is physical VRAM ceiling.
CUDA OOM — Deficit of 24.9 GB
oom45/80 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 3 tok/s).
Throughput
~3 tok/s
333.3 ms / token
Headroom
-24.87 GB
204% of capacity
Layers Fit
35 / 80
45 on DDR5
Max Safe Ctx
0
Tokens before OOM
Host DDR RAM Status:EXCEEDS SYSTEM RAM (28.6 GB required > 32 GB RAM)!
Static Permalink:
Open Static Report/can-i-run/qwen-2-5-72b-instruct-on-rtx-3090Ready-to-Run CLI Commands
# Note: Model requires CPU offloading (35/80 layers on GPU)
ollama run qwen2.5:72bCommands include automatic memory ceiling flags and model layer offload configurations calculated above.
Cloud Hardware Failover
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Hardware Specification Summary
GPU ArchitectureAmpere
Memory Bandwidth936 GB/s
Model Parameters72.7B (LLM)
Attention ArchitectureGrouped-Query (GQA)
Prompt Prefill Speed~410 tok/s (TTFT ~19980ms)
Benchmark Context Tested8,192 tokens
Global Compatibility Matrix for NVIDIA GeForce RTX 3090
Evaluated at Standard Q4_K_M Precision with 8,192 Tokens Context Window
Compatibility Index
75% (43/57)
39 Pass4 Tight14 OOM
| Model & Architecture | Category | Parameters | Required VRAM | Fit Status | Est. Speed | Simulate |
|---|---|---|---|---|---|---|
DeepSeek-R1 (671B Full MoE)MLAMoE DeepSeek | MoE | 671B(37B active) | 411.16 GB | OOM (-387.16G) | ~0.9 tok/s | Simulate |
DeepSeek-V3 (671B Base/Chat)MLAMoE DeepSeek | MoE | 671B(37B active) | 411.16 GB | OOM (-387.16G) | ~0.9 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-70B DeepSeek | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~3.2 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-32B DeepSeek | LLM | 32.8B | 23.75 GB | Tight Fit | ~32.8 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-14B DeepSeek | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~73.2 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-8B DeepSeek | LLM | 8B | 7.33 GB | 100% VRAM Fit | ~134.5 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-8B DeepSeek | LLM | 8.03B | 7.48 GB | 100% VRAM Fit | ~134 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-7B DeepSeek | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~141.6 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-1.5B DeepSeek | LLM | 1.8B | 2.61 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
DeepSeek-Coder-V2 Lite (16B / 2.4B MoE)MLAMoE DeepSeek | MoE | 15.7B(2.4B active) | 11.21 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
DeepSeek-Coder-V2 (236B / 21B MoE)MLAMoE DeepSeek | MoE | 236B(21B active) | 146.23 GB | OOM (-122.23G) | ~3.5 tok/s | Simulate |
Qwen 2.5 Coder 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~3 tok/s | Simulate |
Qwen 2.5 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~3 tok/s | Simulate |
Qwen 2.5 VL 72B (Vision Flagship) Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~3 tok/s | Simulate |
Qwen 2.5 VL 7B (Vision) Qwen | LLM | 7.61B | 6.5 GB | 100% VRAM Fit | ~141.4 tok/s | Simulate |
Qwen 2.5 Coder 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | Tight Fit | ~32.8 tok/s | Simulate |
Qwen 2.5 Coder 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~73.2 tok/s | Simulate |
Qwen 2.5 Coder 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~141.6 tok/s | Simulate |
Qwen 2.5 Coder 1.5B Instruct Qwen | LLM | 1.54B | 2.45 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Qwen 2.5 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | Tight Fit | ~32.8 tok/s | Simulate |
Qwen 2.5 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~73.2 tok/s | Simulate |
Qwen 2.5 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~141.6 tok/s | Simulate |
Qwen 2.5 3B Instruct Qwen | LLM | 3.1B | 3.59 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Qwen 2.5 0.5B Instruct Qwen | LLM | 0.49B | 1.8 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.3 70B Instruct Llama | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~3.2 tok/s | Simulate |
Llama 3.1 70B Instruct Llama | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~3.2 tok/s | Simulate |
Llama 3.1 405B Instruct Llama | LLM | 405B | 252.77 GB | OOM (-228.77G) | ~0.6 tok/s | Simulate |
Llama 3.1 8B Instruct Llama | LLM | 8B | 7.33 GB | 100% VRAM Fit | ~134.5 tok/s | Simulate |
Llama 3.2 3B Instruct Llama | LLM | 3.2B | 4.28 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.2 1B Instruct Llama | LLM | 1.23B | 2.41 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.2 11B Vision Instruct Llama | LLM | 10.6B | 8.92 GB | 100% VRAM Fit | ~101.5 tok/s | Simulate |
Llama 3.2 90B Vision Instruct Llama | LLM | 89B | 59.46 GB | OOM (-35.46G) | ~1.9 tok/s | Simulate |
Microsoft Phi-4 (14B) Phi | LLM | 14.7B | 12.01 GB | 100% VRAM Fit | ~73.2 tok/s | Simulate |
Microsoft Phi-3.5 Mini (3.8B) Phi | LLM | 3.82B | 6.91 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Google Gemma 2 9B Instruct Gemma | LLM | 9.2B | 9.79 GB | 100% VRAM Fit | ~116.9 tok/s | Simulate |
Mistral Small 3 (24B Instruct) Mistral | LLM | 24B | 18.12 GB | 100% VRAM Fit | ~44.8 tok/s | Simulate |
Mistral NeMo (12B Instruct) Mistral | LLM | 12.2B | 10.15 GB | 100% VRAM Fit | ~88.2 tok/s | Simulate |
Mistral 7B Instruct v0.3 Mistral | LLM | 7.25B | 6.88 GB | 100% VRAM Fit | ~148.4 tok/s | Simulate |
Codestral 22B (Mistral AI) Mistral | LLM | 22.2B | 17.02 GB | 100% VRAM Fit | ~48.5 tok/s | Simulate |
Mistral Large 2 (123B) Mistral | LLM | 123B | 79.77 GB | OOM (-55.77G) | ~1.1 tok/s | Simulate |
Mixtral 8x7B (MoE 47B)MoE Mistral | MoE | 46.7B(12.9B active) | 31.15 GB | OOM (-7.15G) | ~27.6 tok/s | Simulate |
Mixtral 8x22B (MoE 141B)MoE Mistral | MoE | 141B(39B active) | 89.67 GB | OOM (-65.67G) | ~3.1 tok/s | Simulate |
Pixtral 12B (Multimodal) Mistral | LLM | 12.4B | 10.28 GB | 100% VRAM Fit | ~86.8 tok/s | Simulate |
Gemma 2 27B Instruct Gemma | LLM | 27.2B | 21.26 GB | Tight Fit | ~39.6 tok/s | Simulate |
Google Gemma 2 2B Instruct Gemma | LLM | 2.6B | 3.84 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
GLM-4 9B Chat (Zhipu AI) GLM | LLM | 9.4B | 7.79 GB | 100% VRAM Fit | ~114.5 tok/s | Simulate |
FLUX.1 [dev] (12B DiT) FLUX | ImageGen | 12B | 15.68 GB | 100% VRAM Fit | ~89.7 tok/s | Simulate |
FLUX.1 [schnell] (4-step) FLUX | ImageGen | 12B | 15.68 GB | 100% VRAM Fit | ~89.7 tok/s | Simulate |
Stable Diffusion XL (SDXL 1.0) SDXL | ImageGen | 3.5B | 7.51 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Stable Diffusion 1.5 (Classic) SD | ImageGen | 1B | 5.78 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Whisper Large v3 (1.5B) Whisper | Audio | 1.5B | 3.64 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Kokoro 82M (Text-to-Speech) Kokoro | Audio | 0.082B | 1.56 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Wan2.1 (14B Text-to-Video) Wan | ImageGen | 14.2B | 18.72 GB | 100% VRAM Fit | ~75.8 tok/s | Simulate |
Wan2.1 (1.3B Video) Wan | ImageGen | 1.3B | 7.28 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
HunyuanVideo (13B Video DiT) Hunyuan | ImageGen | 13B | 19.69 GB | 100% VRAM Fit | ~82.8 tok/s | Simulate |
Stable Diffusion 3.5 Large (8B) SD | ImageGen | 8.1B | 13.3 GB | 100% VRAM Fit | ~132.8 tok/s | Simulate |
BAAI BGE-M3 (Multilingual Embedding) BAAI | LLM | 0.56B | 2.54 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Qwen 2.5 72B Instruct Context Scaling Matrix on NVIDIA GeForce RTX 3090
Dynamic KV Cache footprint and generation throughput measured across sequence lengths (Q4_K_M weights):
| Context Window | KV Cache Memory | Total VRAM Demand | Compatibility Status | Offloaded Layers | Est. Generation Speed |
|---|---|---|---|---|---|
| 2,048 tokens (2k) | 0.66 GB | 46.89 GB / 24 GB | CUDA OOM | 42 layers on RAM | ~3.3 tok/s |
| 4,096 tokens (4k) | 1.33 GB | 47.55 GB / 24 GB | CUDA OOM | 43 layers on RAM | ~3.2 tok/s |
| 8,192 tokens (8k)Standard Baseline | 2.65 GB | 48.87 GB / 24 GB | CUDA OOM | 45 layers on RAM | ~3.0 tok/s |
| 16,384 tokens (16k) | 5.30 GB | 51.52 GB / 24 GB | CUDA OOM | 50 layers on RAM | ~2.6 tok/s |
| 32,768 tokens (32k) | 10.60 GB | 56.82 GB / 24 GB | CUDA OOM | 60 layers on RAM | ~1.8 tok/s |
Hardware Compatibility Q&A: Qwen 2.5 72B Instruct on NVIDIA GeForce RTX 3090
Technical answers for memory allocation, CUDA overhead, and execution parameters.
Can NVIDIA GeForce RTX 3090 run Qwen 2.5 72B Instruct?
CUDA OOM — Deficit of 24.9 GB. 45/80 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 3 tok/s). At an 8k context window, total VRAM demand is 48.87 GB compared to NVIDIA GeForce RTX 3090's 24 GB frame buffer.
How much VRAM does Qwen 2.5 72B Instruct require?
At standard Q4_K_M quantization and 8k context, Qwen 2.5 72B Instruct requires approximately 48.87 GB of VRAM. This includes 44.27 GB for model weights, 2.65 GB for dynamic KV cache (Grouped-Query Attention (GQA)), 0.8 GB for activation buffers, and 1.15 GB for OS display and CUDA runtime overhead.
What generation speed (tokens/sec) can I expect on NVIDIA GeForce RTX 3090?
Estimated autoregressive token generation speed is ~3 tokens per second with a per-token latency of ~333.3 ms, powered by 936 GB/s memory bandwidth. Prompt prefill processing reaches ~410 tok/s (TTFT ~19980 ms).
What causes CUDA Out-of-Memory (OOM) when running Qwen 2.5 72B Instruct?
CUDA OOM occurs when total memory consumption exceeds NVIDIA GeForce RTX 3090's 24 GB physical VRAM. While model weights stay static, the KV cache grows with every conversation token. When VRAM fills up, inference engines (like Ollama or llama.cpp) must offload remaining layers across the PCIe bus (31.5 GB/s) to host system RAM, causing generation speed to drop sharply.
How do I run Qwen 2.5 72B Instruct locally on NVIDIA GeForce RTX 3090?
You can run Qwen 2.5 72B Instruct directly with Ollama using "ollama run qwen-2-5-72b-instruct". For advanced serving with vLLM or llama.cpp, copy the ready-to-run CLI commands in the CLI Commands section above with optimal context, quantization, and GPU layer offload parameters.
Related Hardware Benchmarks & Model Alternatives
Compare Qwen 2.5 72B Instruct and NVIDIA GeForce RTX 3090 against alternative configurations:
Recommended Alternative GPUs for Qwen 2.5 72B Instruct
Explore how Qwen 2.5 72B Instruct performs across other budget, midrange, and flagship architectures:
Other Popular Models You Can Run on NVIDIA GeForce RTX 3090
Verified open-weights LLMs and image models that fit within NVIDIA GeForce RTX 3090's 24GB VRAM:
Dual GPU Showdown: NVIDIA GeForce RTX 3090 vs NVIDIA GeForce RTX 4070 Ti Super
Compare memory bandwidth (936 GB/s vs 672 GB/s), VRAM headroom (24GB vs 16GB), and inference speed head-to-head.
Frequently Asked Questions (FAQ)
Model weights are only one part of total VRAM consumption. When you prompt a model, the KV Cache (Key-Value Cache) stores all historical tokens. At 32k or 64k context, standard GQA attention can demand an additional 4GB to 12GB of VRAM! Combined with activation buffers and OS background consumption (0.8GB on Windows), total memory spikes beyond hardware capacity.