GNER T5 Xxl — Hardware Requirements & GPU Compatibility
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GNER T5 Xxl is a 11.1B-parameter open language model from dyyyyyyyy. At BF16 it needs about 24.50 GB of VRAM — see which GPUs and Macs can run it below.
11 downloads
9 likesSpecifications
- Publisher
- dyyyyyyyy
- Parameters
- 11.1B
- Architecture
- T5ForConditionalGeneration
- Vocabulary Size
- 32,128
- Release Date
- 2024-03-09
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GNER T5 Xxl Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 24.5 GB | — | 22.27 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run GNER T5 Xxl?
BF16 · 24.5 GBGNER T5 Xxl (BF16) requires 24.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 32+ GB is recommended. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run GNER T5 Xxl?
BF16 · 24.5 GB15 devices with unified memory can run GNER T5 Xxl, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does GNER T5 Xxl need?
GNER T5 Xxl requires 24.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 11.1B × 16 bits ÷ 8 = 22.3 GB
KV Cache + Overhead ≈ 2.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1624.5 GB- Can I run GNER T5 Xxl on a Mac?
GNER T5 Xxl requires at least 24.5 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run GNER T5 Xxl locally?
Yes — GNER T5 Xxl can run locally on consumer hardware. At BF16 quantization it needs 24.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GNER T5 Xxl?
At BF16, GNER T5 Xxl can reach ~119 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 24.5 × 0.55 = ~119 tok/s
Estimated speed at BF16 (24.5 GB)
~119 tok/s~89 tok/s~74 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GNER T5 Xxl?
At BF16, the download is about 22.27 GB.
- Which GPUs can run GNER T5 Xxl?
1 consumer GPU can run GNER T5 Xxl at BF16 (24.5 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run GNER T5 Xxl?
15 devices with unified memory can run GNER T5 Xxl at BF16 (24.5 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.