HyperCLOVAX SEED Think 14B — Hardware Requirements & GPU Compatibility
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HyperCLOVAX SEED Think 14B is a 14.7B-parameter open language model from naver-hyperclovax. It supports a context window of up to 131,072 tokens. At BF16 it needs about 30.11 GB of VRAM — see which GPUs and Macs can run it below.
39.9K downloads
119 likesSpecifications
- Publisher
- naver-hyperclovax
- Parameters
- 14.7B
- Architecture
- HyperCLOVAXForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 110,592
- Release Date
- 2025-07-21
- License
- Other
Get Started
HuggingFace
How Much VRAM Does HyperCLOVAX SEED Think 14B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 30.1 GB | 50.2 GB | 29.50 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run HyperCLOVAX SEED Think 14B?
BF16 · 30.1 GBHyperCLOVAX SEED Think 14B (BF16) requires 30.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 40+ GB is recommended. Using the full 131K context window can add up to 20.1 GB, bringing total usage to 50.2 GB. 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 HyperCLOVAX SEED Think 14B?
BF16 · 30.1 GB31 devices with unified memory can run HyperCLOVAX SEED Think 14B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does HyperCLOVAX SEED Think 14B need?
HyperCLOVAX SEED Think 14B requires 30.1 GB of VRAM at BF16. Full 131K context adds up to 20.1 GB (50.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.7B × 16 bits ÷ 8 = 29.5 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 20.7 GB (at full 131K context)
VRAM usage by quantization
BF1630.1 GBBF16 + full context50.2 GB- Can I run HyperCLOVAX SEED Think 14B on a Mac?
HyperCLOVAX SEED Think 14B requires at least 30.1 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 HyperCLOVAX SEED Think 14B locally?
Yes — HyperCLOVAX SEED Think 14B can run locally on consumer hardware. At BF16 quantization it needs 30.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is HyperCLOVAX SEED Think 14B?
At BF16, HyperCLOVAX SEED Think 14B can reach ~159 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 30.1 × 0.65 = ~173 tok/s
Estimated speed at BF16 (30.1 GB)
~173 tok/s~173 tok/s~159 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of HyperCLOVAX SEED Think 14B?
At BF16, the download is about 29.50 GB.
- Which GPUs can run HyperCLOVAX SEED Think 14B?
1 consumer GPU can run HyperCLOVAX SEED Think 14B at BF16 (30.1 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run HyperCLOVAX SEED Think 14B?
35 devices with unified memory can run HyperCLOVAX SEED Think 14B at BF16 (30.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.