Fid Flan T5 Base Nq — Hardware Requirements & GPU Compatibility
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Fid Flan T5 Base Nq is a 248M-parameter open language model from Intel. It supports a context window of up to 512 tokens. At BF16 it needs about 0.54 GB of VRAM — see which GPUs and Macs can run it below.
866 downloads
4 likesSpecifications
- Publisher
- Intel
- Parameters
- 248M
- Architecture
- FusionInDecoderForConditionalGeneration
- Context Length
- 512 tokens
- Vocabulary Size
- 32,128
- Release Date
- 2023-09-27
- License
- cc-by-sa-3.0
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HuggingFace
How Much VRAM Does Fid Flan T5 Base Nq Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 0.5 GB | — | 0.50 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Fid Flan T5 Base Nq?
BF16 · 0.5 GBFid Flan T5 Base Nq (BF16) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Fid Flan T5 Base Nq?
BF16 · 0.5 GB33 devices with unified memory can run Fid Flan T5 Base Nq, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Fid Flan T5 Base Nq need?
Fid Flan T5 Base Nq requires 0.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 248M × 16 bits ÷ 8 = 0.5 GB
VRAM usage by quantization
BF160.5 GB- Can I run Fid Flan T5 Base Nq on a Mac?
Fid Flan T5 Base Nq requires at least 0.5 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 Fid Flan T5 Base Nq locally?
Yes — Fid Flan T5 Base Nq can run locally on consumer hardware. At BF16 quantization it needs 0.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Fid Flan T5 Base Nq?
At BF16, Fid Flan T5 Base Nq can reach ~5398 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~1213 tok/s. 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 ÷ 0.5 × 0.55 = ~5398 tok/s
Estimated speed at BF16 (0.5 GB)
~5398 tok/s~1213 tok/s~4035 tok/s~3338 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Fid Flan T5 Base Nq?
At BF16, the download is about 0.50 GB.
- Which GPUs can run Fid Flan T5 Base Nq?
35 consumer GPUs can run Fid Flan T5 Base Nq at BF16 (0.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Fid Flan T5 Base Nq?
33 devices with unified memory can run Fid Flan T5 Base Nq at BF16 (0.5 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.