# NVIDIA GeForce RTX 4070 for LLM inference and GPU rendering

RTX 4070 12 GB GDDR6 and GDDR6X variants for LLMs and Cycles, including memory inventory and separate server workload phase checks.

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

## Two memory types under one name

NVIDIA lists ordinary RTX 4070 variants with 12 GB GDDR6 and 12 GB GDDR6X, with reference board power of 200 W. This is one model profile with explicit variants rather than two articles changing only memory type. Capacity is per GPU. The name alone does not establish the installed board, so inventory needs its AIB identity and verified memory type. Equal GB does not prove equal transfer or service speed. RTX 4070 SUPER specifications must not automatically replace ordinary 4070 data either: it is another model name with its own documented profile.

| Official variant | Memory per GPU | Memory type | Board power | Evidence |
| --- | --- | --- | --- | --- |
| NVIDIA GeForce RTX 4070 — 12 GB GDDR6X | 12 GB | GDDR6X | 200 W | [Specification](https://www.nvidia.com/en-us/geforce/graphics-cards/compare/) |
| NVIDIA GeForce RTX 4070 — 12 GB GDDR6 | 12 GB | GDDR6 | 200 W | [Specification](https://www.nvidia.com/en-us/geforce/graphics-cards/compare/) |

## Separate inference phases

Match [vLLM requirements](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/) to the installed release and selected kernels. Ada clears the hardware floor while dtype and quantization still require validation. Preserve one checkpoint and observe preparation, prompt processing and output separately on the RTX 4070 variant. When comparing boards, keep input, context, output limit and concurrency equal. CPU offload on one side creates another execution mode. A total duration difference is not automatically caused by GDDR6 versus GDDR6X: first exclude caches, software image, engine settings and host load that can alter results even on the same card. Preserve these conditions with the measurements.

## Evaluate a Cycles scene composition

[Blender Cycles](https://docs.blender.org/manual/en/latest/render/cycles/gpu_rendering.html) describes CUDA and OptiX with their requirements. Select a saved scene for the 12 GB RTX 4070 and pin textures, geometry and quality settings. Verify GPU selection and host RAM use. Do not change the project while comparing memory variants, or the difference becomes uninterpretable. RTX hardware tracing uses a supporting backend rather than certifying every renderer's feature set. Keep preparation duration separate from rendering and inspect the image. Explicitly record completion through system memory instead of treating it as proof that the entire scene fitted locally. Scene identity is part of the experiment.

## Tie comparable workers to inventory

The server record contains GPU identity, AIB model, memory type, driver and environment version. If automated inventory reports only RTX 4070, verify the missing field using that board's documentation. Do not reconstruct it from one observed copy speed. Check supply and cooling for the actual installation; 200 W is neither job energy nor total node power. Queues should preserve the workload manifest when moving jobs between workers. After a driver or engine update, create another result record so a software path change is not incorrectly attributed to the graphics card's memory revision.

## Preserve comparison evidence without a ranking

The matrix contains both variants, model and scene manifests, phase markers, VRAM/RAM and status. If only initialization changes, inspect loading and caching. If GPU phases differ under matched conditions, measure the relevant operation separately. If memory is insufficient, examine residency, KV cache or assets. The profile supplies no acceleration ratio, claim of identical boards or maximum client count. Its output is a verified ordinary RTX 4070 configuration and a reproducible method for the selected inference/render workload, keeping memory type an explicit research input rather than an unstated guess. Additional conclusions need their own controlled evidence.

[Coding LLMs on 12GB VRAM: Budget Context and Headroom](/journal/coding-llm-12gb-vram/) · [LLM memory bandwidth: why gigabytes are not enough](/journal/memory-bandwidth-llm/) · [GPU catalog](/journal/nvidia-amd-gpu-catalog/)