Ollama
Check model-by-model fit, context assumptions, and the verified Windows NVIDIA acceleration path.
Open Ollama guideNVIDIA GeForce RTX 4070 Ti SUPER 16GB · Local AI
RTX 4070 Ti SUPER 16GB for Local AI
The RTX 4070 Ti SUPER 16GB can be a practical local AI card for LLM experiments, image-generation workflows, and CUDA development, as long as model fit and runtime requirements are checked separately.
Hardware summary
- GPU
- NVIDIA GeForce RTX 4070 Ti SUPER 16GB
- VRAM
- 16 GB
- Memory type
- GDDR6X
- Architecture
- Ada Lovelace
- Acceleration
- CUDA
Quick answer
Is the RTX 4070 Ti SUPER 16GB good for local AI?
Yes, 16GB of VRAM can make many local AI configurations possible, especially compared with smaller VRAM cards. It is not a guarantee of speed or universal model fit. Quantization, context length, runtime memory, offloading, system RAM, and the selected app all change what feels practical on a real PC.
Tool overview
What can the RTX 4070 Ti SUPER 16GB run?
LM Studio
Compare recorded GGUF variants against GPU VRAM, then verify CPU, RAM, and offload settings for the full PC.
LM Studio requires AVX2 instruction-set support on Windows x64 systems. LM Studio recommends at least 16 GB of system RAM.
Open LM Studio guideComfyUI
Review SDXL and FLUX workflow memory signals without treating model file size as a VRAM minimum.
Open ComfyUI guidePyTorch
Check the CUDA hardware path separately from your Python, driver, and PyTorch runtime environment.
Open PyTorch guideLocal LLMs
Running local LLMs on an RTX 4070 Ti SUPER 16GB
Local LLM fit is determined by more than the GPU name. AIPCFit keeps model file size, planning VRAM, context support, runtime behavior, and offloading caveats separate so the answer does not collapse into a single unsupported number.
Model / quantization
Context length
GPU VRAM
System RAM
CPU/GPU offloading
Selected runtime
Model compatibility
Local AI models that fit the RTX 4070 Ti SUPER 16GB
These results use the 16GB of dedicated VRAM recorded for this GPU and the shared AIPCFit model-fit engine. They are memory-planning signals, not speed benchmarks or performance guarantees.
Good fit
Meets the catalog's recommended planning-memory level with dedicated GPU VRAM.
22 variants
Llama 3.2 3B
Q4_K_MLlama 3.2 3B · Q4_K_M
- Model-size signal
- 1.7 GB
- Planning memory
- 3.7 GB
16 GB dedicated VRAM meets the 3.7 GB planning memory level for Llama 3.2 3B · Q4_K_M.
Phi-3.5 Mini
Q4_K_MPhi-3.5 Mini · Q4_K_M
- Model-size signal
- 2.2 GB
- Planning memory
- 4.2 GB
16 GB dedicated VRAM meets the 4.2 GB planning memory level for Phi-3.5 Mini · Q4_K_M.
Phi-4 Mini
Q4_K_MPhi-4 Mini · Q4_K_M
- Model-size signal
- 2.2 GB
- Planning memory
- 4.2 GB
16 GB dedicated VRAM meets the 4.2 GB planning memory level for Phi-4 Mini · Q4_K_M.
Gemma 3 4B
Q4_K_MGemma 3 4B · Q4_K_M
- Model-size signal
- 2.49 GB
- Planning memory
- 4.5 GB
16 GB dedicated VRAM meets the 4.5 GB planning memory level for Gemma 3 4B · Q4_K_M.
Qwen3 4B
Q4_K_MQwen3 4B · Q4_K_M
- Model-size signal
- 2.5 GB
- Planning memory
- 4.5 GB
16 GB dedicated VRAM meets the 4.5 GB planning memory level for Qwen3 4B · Q4_K_M.
Llama 3.2 3B
Q8_0Llama 3.2 3B · Q8_0
- Model-size signal
- 3.2 GB
- Planning memory
- 5.2 GB
16 GB dedicated VRAM meets the 5.2 GB planning memory level for Llama 3.2 3B · Q8_0.
DeepSeek-R1 Distill Qwen 7B
Q4_K_MDeepSeek-R1 Distill Qwen 7B · Q4_K_M
- Model-size signal
- 4.1 GB
- Planning memory
- 6.1 GB
16 GB dedicated VRAM meets the 6.1 GB planning memory level for DeepSeek-R1 Distill Qwen 7B · Q4_K_M.
Mistral 7B
Q4_K_MMistral 7B · Q4_K_M
- Model-size signal
- 4.1 GB
- Planning memory
- 6.1 GB
16 GB dedicated VRAM meets the 6.1 GB planning memory level for Mistral 7B · Q4_K_M.
Gemma 3 4B
Q8_0Gemma 3 4B · Q8_0
- Model-size signal
- 4.2 GB
- Planning memory
- 6.2 GB
16 GB dedicated VRAM meets the 6.2 GB planning memory level for Gemma 3 4B · Q8_0.
Showing 9 of 22 variants.
View all 16GB model fitsTight fit
Close to the GPU memory limit and likely to need conservative runtime settings.
1 variant
Mistral Small 3.2 24B
Q4_K_MMistral Small 3.2 24B · Q4_K_M
- Model-size signal
- 15 GB
- Planning memory
- 20.3 GB
16 GB dedicated VRAM is above the 15 GB model-size planning signal but below the load-memory planning level.
This GPU page does not assume how much system RAM your PC has, so partial-offload configurations are intentionally not ranked here.
Check my full PCVRAM capacity
What does 16GB of VRAM mean for local AI?
VRAM capacity affects how much model data, runtime state, and working memory can remain on the GPU. Quantization can reduce memory pressure, while longer context windows, higher image resolutions, loaded auxiliary models, and runtime settings can increase it. When GPU memory is not enough, system RAM and CPU/GPU offloading may become part of the configuration.
Explore local model variants for 16GB VRAMWorkloads
Practical RTX 4070 Ti SUPER 16GB local AI paths
Local LLMs
Use the Ollama and LM Studio pages to compare recorded model variants, planning VRAM, context assumptions, and offload caveats.
Check local LLM fitImage generation / ComfyUI
Use the ComfyUI page to compare recorded workflow signals and understand when resolution, precision, and nodes may change memory pressure.
Check ComfyUI fitPyTorch / CUDA workloads
Use the PyTorch page to verify the hardware CUDA path while keeping software environment checks separate.
Check PyTorch pathInteractive checks
Reuse the same checks as the detailed pages
These widgets use the existing AIPCFit model and workflow fit logic for this exact GPU.
Interactive check
Try a model on this GPU
Change the model or context assumption and see how the hardware-fit signal changes. This does not predict speed or model quality.
Hardware fit
Good hardware fitGPU VRAM
16 GB
Planning VRAM
6.7 GB
Context
8K supported
What this means
The GPU has enough VRAM for the model weights plus the conservative planning headroom used by this tool.
Model file size and planning VRAM are not official VRAM minimums. Exact memory use changes with context, runtime behavior, and offloading.
Interactive GPU check
Try an LM Studio GGUF model
This checks GPU VRAM fit only. Full LM Studio compatibility also depends on the Windows x64 AVX2 CPU prerequisite and system RAM.
GPU fit
Good GPU fitGGUF file
5.03 GB
Planning VRAM
~6.5 GB
What this means
- The GPU has enough VRAM for the GGUF file plus the conservative planning headroom used by AIPCFit.
- Actual LM Studio memory usage still depends on context length and load configuration.
- LM Studio's own estimator should be used for an exact load-time estimate.
Interactive check
Try a ComfyUI workflow on this GPU
Compare the current verified image workflows against this GPU's recorded VRAM. Model file size is not treated as an official VRAM requirement.
Memory signal
Comfortable planning headroomGPU VRAM
16 GB
Core model file
6.94 GB
Precision
fp16
What this means
The core model file is smaller than this GPU's VRAM with the planning buffer used by this tool.
Actual memory use changes with resolution, batch size, VAE, ControlNet, other nodes, loaded models, and ComfyUI's memory management.
Common questions
Can the RTX 4070 Ti SUPER 16GB run local LLMs?
It can be useful for local LLM workloads, but exact fit depends on the selected model, quantization, context length, runtime memory, system RAM, and offloading. AIPCFit checks those signals without claiming a universal model-size limit.
Is 16GB VRAM enough for Ollama?
It can be enough for some Ollama configurations, but not every model or context setting. The RTX 4070 Ti SUPER 16GB Ollama page uses the recorded model catalog and context checks to show fit signals without predicting speed.
Can I use LM Studio with the RTX 4070 Ti SUPER 16GB?
GPU VRAM is one part of LM Studio model fit. Full compatibility can also depend on the operating system, CPU support, system RAM, and selected GPU offload settings.
Can the RTX 4070 Ti SUPER 16GB run ComfyUI?
Use the detailed ComfyUI page to check the verified compatibility path and workflow-fit signals. Model choice, precision, resolution, nodes, loaded models, and memory management can all affect practical fit.
Does PyTorch support the RTX 4070 Ti SUPER 16GB?
Use the detailed PyTorch page to verify the hardware CUDA path. Your actual Python version, PyTorch build, CUDA runtime, NVIDIA driver, and workload remain separate environment checks.
Is 16GB VRAM the same as the maximum model file size?
No. Model file size is not the same thing as required VRAM. Runtime memory, context length, offloading, precision, and runtime behavior can all change memory use.
Evidence and trust
Unknown stays unknown.
AIPCFit starts from verified structured hardware data, applies deterministic rules, and explains uncertainty instead of filling gaps with benchmark guesses.