BENCHMARK REPORT
Can I Run Qwen 2.5 Coder 14B Instruct on NVIDIA GeForce GTX 1080 Ti?
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 Coder 14B Instruct on NVIDIA GeForce GTX 1080 Ti
Authoritative verdict, memory allocation, and token generation performance:
Direct Answer: CUDA OOM — Deficit of 0.9 GBQwen 2.5 Coder 14B Instruct exceeds dedicated physical VRAM and triggers PCIe layer offloading on NVIDIA GeForce GTX 1080 Ti (11GB). At standard Q4_K_M 4-bit quantization and an 8,192-token context window, total memory demand is 11.94 GB (8.95 GB weights + 1.59 GB dynamic KV cache + 1.15 GB OS & CUDA buffers). Physical VRAM is exceeded by 0.94 GB, forcing 6 transformer layers to offload across the PCIe bus to host DDR RAM. Estimated generation throughput is ~15.2 tokens/sec (~65.8 ms per token) with prompt prefill processing reaching ~161 tok/s.
Run Verdict
CUDA OOM (Offload)
Total Required VRAM
11.94 GB / 11 GB
Decoding Speed
~15.2 tok/s (65.8ms)
Safe Context Window
Offload to RAM
Hardware Architecture & VRAM Topology
PascalNVIDIA GeForce GTX 1080 Ti • 484 GB/s Bandwidth
Token Generation
~15.2 tok/s(65.8 ms)
OOM SPILL
VRAM Utilization Breakdown11.94 GB / 11 GB (109%)
VRAM Limit (11G)
8.95G
1.59G
1.15G
-0.94G Spill
Weights
8.95 GB
KV Cache
1.59 GB
Activation
0.25 GB
OS/Driver
1.15 GB
Overflow
0.94 GB
Hardware Chokepoint: PCIe Bus Throttling Active
GPU VRAM (11 GB)
42 / 48 Layers Loaded
484 GB/s Bus
PCIe 4.0 x16 Bottleneck
31.5 GB/s
-97% Bandwidth Drop
Host DDR5 System RAM
6 Layers Offloaded
+1.3 GB RAM Siphoned
Why does your generation speed collapse to ~15.2 tok/s? Because for every single generated token, model weights across the 6 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.Max Safe: 3,337 tokens
CUDA OOM — Deficit of 0.9 GB
oom6/48 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 15.2 tok/s).
Throughput
~15.2 tok/s
65.8 ms / token
Headroom
-0.94 GB
109% of capacity
Layers Fit
42 / 48
6 on DDR5
Max Safe Ctx
3k
Tokens before OOM
Host DDR RAM Status:Fits in DDR RAM (+1.3 GB used of 32 GB)
Static Permalink:
Open Static Report/can-i-run/qwen-2-5-coder-14b-on-gtx-1080-tiReady-to-Run CLI Commands
# Note: Model requires CPU offloading (42/48 layers on GPU)
ollama run qwen2.5-coder:14bCommands include automatic memory ceiling flags and model layer offload configurations calculated above.
Cloud Hardware Failover
gpuy.dev On-Demand ComputeDon't spend $2,000+ on hardware — Deploy Qwen 2.5 Coder 14B Instruct in 60s
Your local NVIDIA GeForce GTX 1080 Ti is missing 0.94 GB of VRAM. Running on CPU RAM throttles speeds to ~15.2 tok/s. Spin up a cloud instance starting from $0.35/hr.
4x Faster Speed
$0.35/hr
Breakeven: ~5,286 hrs vs hardware purchase
Per-second billing
Launch Cloud Node →Pre-configured environment
Hardware Specification Summary
GPU ArchitecturePascal
Memory Bandwidth484 GB/s
Model Parameters14.7B (LLM)
Attention ArchitectureGrouped-Query (GQA)
Prompt Prefill Speed~161 tok/s (TTFT ~50882ms)
Benchmark Context Tested8,192 tokens
Global Compatibility Matrix for NVIDIA GeForce GTX 1080 Ti
Evaluated at Standard Q4_K_M Precision with 8,192 Tokens Context Window
Compatibility Index
47% (27/57)
24 Pass3 Tight30 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 (-400.16G) | ~0.6 tok/s | Simulate |
DeepSeek-V3 (671B Base/Chat)MLAMoE DeepSeek | MoE | 671B(37B active) | 411.16 GB | OOM (-400.16G) | ~0.6 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-70B DeepSeek | LLM | 70.6B | 47.6 GB | OOM (-36.6G) | ~0.6 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-32B DeepSeek | LLM | 32.8B | 23.75 GB | OOM (-12.75G) | ~2.9 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-14B DeepSeek | LLM | 14.7B | 11.94 GB | OOM (-0.94G) | ~15.2 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-8B DeepSeek | LLM | 8B | 7.33 GB | 100% VRAM Fit | ~69.5 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-8B DeepSeek | LLM | 8.03B | 7.48 GB | 100% VRAM Fit | ~69.3 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-7B DeepSeek | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~73.2 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 | OOM (-0.21G) | ~102.2 tok/s | Simulate |
DeepSeek-Coder-V2 (236B / 21B MoE)MLAMoE DeepSeek | MoE | 236B(21B active) | 146.23 GB | OOM (-135.23G) | ~1 tok/s | Simulate |
Qwen 2.5 Coder 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-37.87G) | ~0.6 tok/s | Simulate |
Qwen 2.5 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-37.87G) | ~0.6 tok/s | Simulate |
Qwen 2.5 VL 72B (Vision Flagship) Qwen | LLM | 72.7B | 48.87 GB | OOM (-37.87G) | ~0.6 tok/s | Simulate |
Qwen 2.5 VL 7B (Vision) Qwen | LLM | 7.61B | 6.5 GB | 100% VRAM Fit | ~73.1 tok/s | Simulate |
Qwen 2.5 Coder 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | OOM (-12.75G) | ~2.9 tok/s | Simulate |
Qwen 2.5 Coder 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | OOM (-0.94G) | ~15.2 tok/s | Simulate |
Qwen 2.5 Coder 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~73.2 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 | OOM (-12.75G) | ~2.9 tok/s | Simulate |
Qwen 2.5 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | OOM (-0.94G) | ~15.2 tok/s | Simulate |
Qwen 2.5 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~73.2 tok/s | Simulate |
Qwen 2.5 3B Instruct Qwen | LLM | 3.1B | 3.59 GB | 100% VRAM Fit | ~179.5 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 (-36.6G) | ~0.6 tok/s | Simulate |
Llama 3.1 70B Instruct Llama | LLM | 70.6B | 47.6 GB | OOM (-36.6G) | ~0.6 tok/s | Simulate |
Llama 3.1 405B Instruct Llama | LLM | 405B | 252.77 GB | OOM (-241.77G) | ~0.6 tok/s | Simulate |
Llama 3.1 8B Instruct Llama | LLM | 8B | 7.33 GB | 100% VRAM Fit | ~69.5 tok/s | Simulate |
Llama 3.2 3B Instruct Llama | LLM | 3.2B | 4.28 GB | 100% VRAM Fit | ~173.9 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 | ~52.5 tok/s | Simulate |
Llama 3.2 90B Vision Instruct Llama | LLM | 89B | 59.46 GB | OOM (-48.46G) | ~0.6 tok/s | Simulate |
Microsoft Phi-4 (14B) Phi | LLM | 14.7B | 12.01 GB | OOM (-1.01G) | ~15.2 tok/s | Simulate |
Microsoft Phi-3.5 Mini (3.8B) Phi | LLM | 3.82B | 6.91 GB | 100% VRAM Fit | ~145.6 tok/s | Simulate |
Google Gemma 2 9B Instruct Gemma | LLM | 9.2B | 9.79 GB | Tight Fit | ~60.5 tok/s | Simulate |
Mistral Small 3 (24B Instruct) Mistral | LLM | 24B | 18.12 GB | OOM (-7.12G) | ~5.5 tok/s | Simulate |
Mistral NeMo (12B Instruct) Mistral | LLM | 12.2B | 10.15 GB | Tight Fit | ~45.6 tok/s | Simulate |
Mistral 7B Instruct v0.3 Mistral | LLM | 7.25B | 6.88 GB | 100% VRAM Fit | ~76.7 tok/s | Simulate |
Codestral 22B (Mistral AI) Mistral | LLM | 22.2B | 17.02 GB | OOM (-6.02G) | ~6.5 tok/s | Simulate |
Mistral Large 2 (123B) Mistral | LLM | 123B | 79.77 GB | OOM (-68.77G) | ~0.6 tok/s | Simulate |
Mixtral 8x7B (MoE 47B)MoE Mistral | MoE | 46.7B(12.9B active) | 31.15 GB | OOM (-20.15G) | ~6 tok/s | Simulate |
Mixtral 8x22B (MoE 141B)MoE Mistral | MoE | 141B(39B active) | 89.67 GB | OOM (-78.67G) | ~0.7 tok/s | Simulate |
Pixtral 12B (Multimodal) Mistral | LLM | 12.4B | 10.28 GB | Tight Fit | ~44.9 tok/s | Simulate |
Gemma 2 27B Instruct Gemma | LLM | 27.2B | 21.26 GB | OOM (-10.26G) | ~3.6 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 | ~59.2 tok/s | Simulate |
FLUX.1 [dev] (12B DiT) FLUX | ImageGen | 12B | 15.68 GB | OOM (-4.68G) | ~11.8 tok/s | Simulate |
FLUX.1 [schnell] (4-step) FLUX | ImageGen | 12B | 15.68 GB | OOM (-4.68G) | ~11.8 tok/s | Simulate |
Stable Diffusion XL (SDXL 1.0) SDXL | ImageGen | 3.5B | 7.51 GB | 100% VRAM Fit | ~158.9 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 | OOM (-7.72G) | ~7.9 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 | OOM (-8.69G) | ~8.1 tok/s | Simulate |
Stable Diffusion 3.5 Large (8B) SD | ImageGen | 8.1B | 13.3 GB | OOM (-2.3G) | ~22.6 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 Coder 14B Instruct Context Scaling Matrix on NVIDIA GeForce GTX 1080 Ti
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) | 0.40 GB | 10.75 GB / 11 GB | Tight Headroom | 0 (All in VRAM) | ~37.8 tok/s |
| 4,096 tokens (4k) | 0.80 GB | 11.15 GB / 11 GB | CUDA OOM | 1 layers on RAM | ~17.0 tok/s |
| 8,192 tokens (8k)Standard Baseline | 1.59 GB | 11.94 GB / 11 GB | CUDA OOM | 6 layers on RAM | ~15.2 tok/s |
| 16,384 tokens (16k) | 3.18 GB | 13.53 GB / 11 GB | CUDA OOM | 14 layers on RAM | ~12.4 tok/s |
| 32,768 tokens (32k) | 6.36 GB | 16.71 GB / 11 GB | CUDA OOM | 31 layers on RAM | ~6.5 tok/s |
Hardware Compatibility Q&A: Qwen 2.5 Coder 14B Instruct on NVIDIA GeForce GTX 1080 Ti
Technical answers for memory allocation, CUDA overhead, and execution parameters.
Can NVIDIA GeForce GTX 1080 Ti run Qwen 2.5 Coder 14B Instruct?
CUDA OOM — Deficit of 0.9 GB. 6/48 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 15.2 tok/s). At an 8k context window, total VRAM demand is 11.94 GB compared to NVIDIA GeForce GTX 1080 Ti's 11 GB frame buffer.
How much VRAM does Qwen 2.5 Coder 14B Instruct require?
At standard Q4_K_M quantization and 8k context, Qwen 2.5 Coder 14B Instruct requires approximately 11.94 GB of VRAM. This includes 8.95 GB for model weights, 1.59 GB for dynamic KV cache (Grouped-Query Attention (GQA)), 0.25 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 GTX 1080 Ti?
Estimated autoregressive token generation speed is ~15.2 tokens per second with a per-token latency of ~65.8 ms, powered by 484 GB/s memory bandwidth. Prompt prefill processing reaches ~161 tok/s (TTFT ~50882 ms).
What causes CUDA Out-of-Memory (OOM) when running Qwen 2.5 Coder 14B Instruct?
CUDA OOM occurs when total memory consumption exceeds NVIDIA GeForce GTX 1080 Ti's 11 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 Coder 14B Instruct locally on NVIDIA GeForce GTX 1080 Ti?
You can run Qwen 2.5 Coder 14B Instruct directly with Ollama using "ollama run qwen-2-5-coder-14b". 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 Coder 14B Instruct and NVIDIA GeForce GTX 1080 Ti against alternative configurations:
Recommended Alternative GPUs for Qwen 2.5 Coder 14B Instruct
Explore how Qwen 2.5 Coder 14B Instruct performs across other budget, midrange, and flagship architectures:
Other Popular Models You Can Run on NVIDIA GeForce GTX 1080 Ti
Verified open-weights LLMs and image models that fit within NVIDIA GeForce GTX 1080 Ti's 11GB VRAM:
Dual GPU Showdown: NVIDIA GeForce GTX 1080 Ti vs NVIDIA GeForce RTX 4090
Compare memory bandwidth (484 GB/s vs 1008 GB/s), VRAM headroom (11GB vs 24GB), 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.