What GPU do I need to run nvidia/NVIDIA-NemotronLabs-VoiceChat-11B?
11.1B parameters, published in F32. View on Hugging Face
Set up NVIDIA-Nemo…bs-VoiceChat-11BNVIDIA-NemotronLabs-VoiceChat-11B is published by nvidia on Hugging Face, with 3,567 downloads and 450 likes to date. It's a unlisted-architecture model built for text-generation, published natively in F32.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 41.3 GB | 49.6 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | V100 | 4 | $0.352/hr | ||
| FP8 (quantized) | 10.3 GB | 12.4 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 5.2 GB | 6.2 GB | RTX 4070 Super | 1 | $0.121/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run NVIDIA-NemotronLabs-VoiceChat-11B at its published (F32) precision: 1× A100, at $1.21/hr per GPU ($1.21/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
NVIDIA-NemotronLabs-VoiceChat-11B: common questions
Can NVIDIA-NemotronLabs-VoiceChat-11B run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 49.6 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
Can NVIDIA-NemotronLabs-VoiceChat-11B run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 41.3 GB, or 49.6 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 20.7 GB, or 24.8 GB with overhead. That moves it onto a 32 GB card, which the FP32 weights do not fit. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM NVIDIA-NemotronLabs-VoiceChat-11B can run in?
6.2 GB, at INT4 (quantized), which fits an 8 GB card, against 49.6 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing NVIDIA-NemotronLabs-VoiceChat-11B lower the GPU bill?
Yes. At FP32 the cheapest live fit is one A100 at $1.21/hr. At INT4 (quantized) it drops to one RTX 4070 Super at $0.121/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Lineage
Based on NVIDIA-Nemotron-Nano-9B-v2.
More on NVIDIA-NemotronLabs-VoiceChat-11B
Fits on an 80 GB GPU at FP32: every model that fits in 80 GB.
Fits on a 16 GB GPU at FP8: every model that fits in 16 GB.
Fits on an 8 GB GPU at INT4: every model that fits in 8 GB.
Related reading: A100 pricing and specs, How much VRAM you need for LLMs, and What AI inference is.
