GTX 10ConsumerPascalPCIe 3CUDA
Operating mode
Choose the operating mode for this hardware
Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
About this GPU for AI
The GTX 1080 Ti 11GB was once NVIDIA's flagship consumer card, and it remains usable for local AI via llama.cpp or Ollama with quantized models. Its 11 GB of VRAM can fit 7B models at Q4 and occasionally a 13B model at Q3 — modest by modern standards. Crucially, Pascal lacks Tensor Cores entirely (CUDA compute capability 6.1), meaning no INT8 acceleration. More importantly, NVIDIA has announced Pascal support will be dropped from future CUDA versions (post-12.x), putting a clear end-of-life timeline on its AI usefulness.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
legacy-but-capablelimited-vramcuda-deprecation-riskbudget-used-market
Specifications
Compute
FP1622 TFLOPS
INT888 TOPS
ArchitecturePascal
Memory
VRAM11 GB
Bandwidth484 GB/s
General
FamilyGTX 10
SegmentConsumer
InterconnectPCIe 3
Compute PlatformCUDA
MSRP$699
Key Features
CUDA Compute Capability 6.1 (Pascal) — no Tensor Cores484 GB/s memory bandwidth (GDDR5X)11 GB GDDR5X VRAMPCIe Gen 3 x16CUDA toolkit support ending with post-12.x deprecationNo INT8/FP16 Tensor Core acceleration
For AI Workloads
Strengths
- 11 GB VRAM allows 7B models at Q4 and limited 13B models at Q3
- Still works with llama.cpp and Ollama for quantized inference today
- Very cheap on the used market
- Reasonable bandwidth (484 GB/s) for a Pascal-era card
Considerations
- No Tensor Cores — inference runs on CUDA cores only, much slower than RTX-era GPUs
- CUDA 13.x will drop Pascal support, making it increasingly incompatible with new frameworks
- vLLM and TGI already require compute capability 7.0+ — excluded from these frameworks
- Pascal efficiency factor (0.59) reflects poor inference-per-compute characteristics
Pascal is NVIDIA's first 16nm FinFET GPU architecture, powering the GTX 10-series consumer cards and Tesla P100/P40 datacenter accelerators. It introduced unified memory architecture and NVLink interconnect for datacenter GPUs.
AI Relevance
No dedicated Tensor Cores — all AI inference runs on standard CUDA cores at FP16 or FP32 precision. Still usable for small models (7B Q4) on cards with sufficient VRAM like the GTX 1080 Ti (11 GB) or P40 (24 GB), but significantly slower than Turing and newer.
Process: TSMC 16nmPlatform: CUDAPrecisions: FP32, FP16
Recommendations by Workload
Qwen 3.5 9B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 55.9 tok/s · 26K ctx · llama.cppEST.
CodeGeeX 4 9B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 56.9 tok/s · 92K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
30.5BTier 100Needs ~21.3 GB
397BTier 100Needs ~245.6 GB
123BTier 100Needs ~79.7 GB
1000BTier 100Needs ~615.7 GB
1000BTier 100Needs ~615.7 GB
Image & Video Generation
Diffusion Model Compatibility
23 of 52 models can generate images or video on your GTX 1080 Ti 11GB
Upgrade paths
Upgrade from GTX 1080 Ti 11GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
11
GB
GTX 1080 Ti 11GBCategory AvgRTX 3060 12GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~19.2s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~1m 26s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~1m 46s per image |
| Video Short (25f) | Runs with offload | LTX Video 2B | ~~16.7s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~49.1s/frame |
CodeGeeX 4 9B is a specialized fit for Agentic Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 56.9 tok/s · 92K ctx · llama.cppEST.
Qwen 3.5 9B matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 55.9 tok/s · 26K ctx · llama.cppEST.
CodeGeeX 4 9B is viable for RAG, but is not the most specialized choice. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 56.9 tok/s · 92K ctx · llama.cppEST.
8B
9.1 GB
63 tok/s
30K ctx
Image
| MAGI-1Video | 256×256 | ~45.1s/frame | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
There are 4 upgrade path(s) from GTX 1080 Ti 11GB: RTX 3060 12GB, RTX 4070 12GB. Upgrading would unlock larger models and faster inference speeds.
Buying advice
Should you buy GTX 1080 Ti 11GB for local AI?
Usable for local AI with limits
Can run 9 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
What will limit you first
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best upgrade itinerary
Unlocks 3 additional models that do not fit on the current setup.
Want more headroom? RTX 3060 12GB (12.0 GB VRAM) is the next step up.
