In this article
Compact Turing coverage requires per-SKU capacities and deliberately small qualification cases.
NVIDIA T400, T600 and T1000 is assessed here as an exact Turing board configuration. Its published 2/4/4/4/8 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
These compact Turing cards share a family context but different capacities: T400 2/4 GB, T600 4 GB and T1000 4/8 GB. The table keeps every exact SKU visible.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| NVIDIA T400 2 GB | 2 GB GDDR6 | 30 W | Active; Low-profile single slot |
| NVIDIA T400 4 GB | 4 GB GDDR6 | 30 W | Active; Low-profile single slot |
| NVIDIA T600 4 GB | 4 GB GDDR6 | 40 W | Active; Low-profile single slot |
| NVIDIA T1000 4 GB | 4 GB GDDR6 | 50 W | Active; Low-profile single slot |
| NVIDIA T1000 8 GB | 8 GB GDDR6 | 50 W | Active; Low-profile single slot |
Specifications: NVIDIA product specification 1, NVIDIA product specification 2, NVIDIA product specification 3.
LLM backend and memory budget
All three are Turing, but they lack the Tensor/RT hardware implied by the RTX brand. Their architecture can meet the vLLM minimum while tiny VRAM makes many actual serving configurations impractical; assess GGUF size, context, runtime and partial offload per variant.
Passing the architecture requirement does not certify every model, quantization kernel or prebuilt package.
Compatibility evidence: vLLM GPU requirements.
Cycles and batch rendering
Compare CUDA/OptiX small-scene feasibility and sustained thermal behavior by the exact board. Renderer architecture eligibility is distinct from hardware RT acceleration, scene fit and denoising overhead. For the 2 GB variant, runtime and denoising memory may leave too little room for the actual scene.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
All listed boards are active low-profile single-slot designs, but the published limits differ: 30 W T400, 40 W T600 and 50 W T1000. Preserve actual memory capacity; the 4 GB and 8 GB T1000 must not share an unqualified workload label.
A concrete qualification scenario
An office inventory contains all three names. Create one table row per physical capacity and choose one tiny control model before exploring larger cases. Record whether each board loads the runtime, offloads weights and completes the prompt. Render an independently chosen small scene afterward. This profile compares eligibility and failure stages, without inventing a ranking from CUDA-core counts or treating a 2 GB result as representative of 8 GB.
The evidence to retain
Keep an ordinary qualification record with the actual test outputs.
Identify the tested variant, software build and execution mode before entering observations. Leave unavailable measurements explicitly unknown.
The fields below separate published specifications from observed allocation and job completion, so a later operator can repeat the same case.
| Evidence field | What this profile must retain |
|---|---|
| Hardware identity | NVIDIA T400, T600 and T1000; physical variant and reported device identifier |
| Memory boundary | 2/4/4/4/8 GB published; observed free memory before and after loading |
| Inference artifact | Exact weights digest, quantization, context and offload configuration |
| Render artifact | Scene digest, renderer version, backend and denoising setting |
| System envelope | Driver, container/build digest, enclosure and cooling configuration |
| Completion evidence | Application exit status, output validity and observed memory failures |
The 2 GB T400 result should not be transferred to 4 GB T600 or8 GB T1000; retain the exact table row in every observation.
Further reading: Build llama.cpp CUDA binaries for multiple GPU architectures, Coding LLMs on 8 GB VRAM: Start with a Bounded Task, NVIDIA and AMD GPU catalogue.