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The ordinary RTX 4060 has 8 GB; verify container device access and a suitable runtime before judging model and scene memory.
Identify the ordinary RTX 4060
The RTX 4060 is a desktop Ada Lovelace model with 8 GB GDDR6 and reference board power of 115 W. It is not interchangeable with the RTX 4060 Ti, which has other capacity variants. The table describes memory per GPU. Before transferring a job, preserve actual identity and AIB design. Dimensions, cooling and supply connections depend on the board. Nominal 8 GB does not show the remainder with a display or another process running. Capture initial inventory on the host and inside the intended worker environment before loading model weights or scene assets.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| NVIDIA GeForce RTX 4060 — 8 GB NVIDIA comparison design | 8 GB | GDDR6 | 115 W | Specification |
Check runtime before model size
The vLLM documentation describes GPU and installation requirements. Ada hardware eligibility cannot correct missing container access or an unsupported backend. Confirm the intended RTX 4060 and runtime version first. Then launch a fixed checkpoint with bounded context and output. A detection failure should not trigger blindly shrinking the model: it belongs to another diagnostic branch. If the device is available but memory is insufficient, inspect weights, KV cache and temporary structures. For llama.cpp, preserve CUDA build options and actual GPU layer placement separately alongside host RAM use.
Transfer the scene with its assets
Test Cycles CUDA or OptiX using the intended Blender version and an appropriate driver. An environment must do more than see the RTX 4060: it should render the saved project successfully. Include textures, geometry and settings, then inspect image and logs. If one worker reads a missing asset from another directory, the experiment is not reproducible from the transferred project alone. Record host memory use separately. RTX hardware tracing concerns a supporting backend rather than certifying every renderer, material and feature in another software system. Keep the packed artifact as the unit of acceptance.
Describe the worker resource
The worker manifest contains image version, driver boundary, device mapping and input/output paths. Assign jobs to an explicit GPU instead of assuming enumeration indices remain identical after a host change. A resident model can consume memory with no active requests. Check its state and switching policy before a render job. The 115 W specification describes the reference board, not an entire container or server. On a multi-device host, separate processes must not accidentally contend for one card because of faulty mapping or identifier normalization in the queue system. Observe which process actually owns memory during each phase.
Preserve a portability record
Compare one manifest and matching input files across the intended worker environments. Each row records detected GPU model, versions, status, memory and output hash. If only detection differs, inspect the environment; if source files differ, inspect asset packaging. If both are correct but memory constrains execution, record the 8 GB workload envelope. This profile promises neither identical speed across containers nor absence of CPU offload or fit for every small LLM. It identifies a reproducible task on the ordinary RTX 4060 without substituting Ti or Laptop product specifications. Retain failure classification alongside successful cases.
Coding LLMs on 8GB VRAM: Start with a Bounded Task · Display-attached GPUs: the VRAM baseline before model loading · GPU catalog