# NVIDIA GeForce RTX 4070 Ti for LLM inference and GPU rendering

RTX 4070 Ti 12 GB for inference and Cycles: the original non-SUPER profile, context memory, reference 285 W and complete job energy boundaries.

Author: Retrivium team
Updated: 2026-11-02
Canonical URL: https://retrivium.ai/journal/gpu-nvidia-rtx-4070-ti-inference-rendering/
Language: en

## Identify the original 12 GB Ti

The RTX 4070 Ti uses 12 GB GDDR6X and has reference board power of 285 W. The Ti SUPER differs, including its 16 GB capacity. Equal specified watts do not make them interchangeable resident workload resources. The table gives memory per GPU. When assessing an installed Ti, preserve board identity, free VRAM and the actual environment. A record containing only 4070 Ti must not automatically inherit a SUPER manifest: confirm exact identity and hardware variant before interpreting the task budget. Nominal capacity and observed availability remain separate pieces of evidence.

| Official variant | Memory per GPU | Memory type | Board power | Evidence |
| --- | --- | --- | --- | --- |
| NVIDIA GeForce RTX 4070 Ti — 12 GB NVIDIA comparison design | 12 GB | GDDR6X | 285 W | [Specification](https://www.nvidia.com/en-us/geforce/graphics-cards/compare/) |

## Separate prefill from model output

The [vLLM documentation](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/) requires an appropriate hardware and software path. Check checkpoint, dtype and selected Ada backend separately; CUDA availability does not certify every operation. An RTX 4070 Ti experiment keeps history and output limit fixed while observing request phases. Account for weights and KV cache separately from visible text. A request ending at an output limit does not necessarily indicate insufficient VRAM. Vary prompt length in another series so changes in duration or memory can be attributed to an understandable factor rather than several service parameters changing at once.

## Measure a complete Cycles job

Validate [Cycles CUDA or OptiX](https://docs.blender.org/manual/en/latest/render/cycles/gpu_rendering.html) with a scene containing saved assets and settings. Record project loading, preparation and image completion on the Ti. An internal GPU interval does not describe the entire server job duration. If data exceeds local memory, observe host RAM and mark the different execution mode. Supporting backends can use RTX tracing, but that promises nothing about another renderer's features. A passing scene confirms the examined materials and geometry only. Preserve the result file, backend log and manifest together instead of replacing them with one general GPU performance score.

## Distinguish watts from energy

For integration, 285 W helps interpret the board specification, while energy depends on actual work and measurement boundary. GPU telemetry is not automatically CPU, RAM and storage consumption. One power snapshot also cannot compare a short burst with a different long job. Record acceptance-to-completion interval, observation source and active process composition. Verify supply, connectors and cooling against the AIB model. This experiment needs no clock changes: assess normal system behavior and whether the existing configuration supports the selected workload first. A whole node claim requires observations at that broader boundary.

## Decide using matched workload files

The artifact contains LLM fixtures, the scene, phase markers, memory and original power observations. If a complete job is slow despite a short GPU phase, inspect loading and preparation. If long histories increase memory, examine the inference budget; if only the scene does, investigate assets. Comparing Ti SUPER requires another experiment using the same files and conditions. This profile prescribes no token rate, request cost or guaranteed model size. It describes the original RTX 4070 Ti as a specific 12 GB resource with operational boundaries that can be checked and reproduced.

[Coding LLMs on 12GB VRAM: Budget Context and Headroom](/journal/coding-llm-12gb-vram/) · [GPU power caps: an energy and latency experiment](/journal/gpu-power-cap-latency-frontier/) · [GPU catalog](/journal/nvidia-amd-gpu-catalog/)