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.
Doesn't fit a 24 GB GPU
Smallest consumer fit → RTX 5090 · BF16 · 32.3 GB · ~39 tok/s
Macs from 36 GB (BF16 · ~11 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run HyperCLOVAX SEED Think 14B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve naver-hyperclovax/HyperCLOVAX-SEED-Think-14B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on A100 40GB (7 GB headroom) · BF16
- Generation speed
- ~34 tok/s
- generation speed
- Cost per 1M output tokens
- $5.04
- per 1M output tokens
How Much VRAM Does HyperCLOVAX SEED Think 14B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 30.1 GB | 34.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 32.3 GB |
Selected BF16
Brain floating point 1632.3GBat 16K context
- Weights
- 29.5 GB
- KV cache
- 2.6 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 8.3 GB more
Or .
Which Hardware 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. 78 devices with unified memory can run HyperCLOVAX SEED Think 14B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (48 GB).
32–40 GB
tight · up to 8K ctx
1 GPU · 2 Mac classes · 1 device family
RTX 5090~39Simulate output on RTX 5090M5 Max36 GB~11Simulate output on Mac Studio M5 Max (36 GB)M3 Pro*36 GB~4Simulate output on MacBook Pro 14" M3 Pro (36 GB)Orin~4Simulate output on NVIDIA Jetson AGX Orin 32GB48 GB
48 GB tier →fits · 15.7 GB free · up to 64K ctx
2 Mac classes · 1 device family
M5 Max48 GB~14Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~7Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~5Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Phi 4 ReasoningMicrosoft | — | 12.5 GB | ~69 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Qwen1.5 14BAlibaba | — | 22.2 GB | ~63 | same size |
| Nemotron Terminal 14BNVIDIA | — | 11.9 GB | ~69 | same size |
| Ministral 3 14B Reasoning 2512Mistral AI | — | 12 GB | ~72 | same size |
| Qwen3 14B BaseAlibaba | — | 11.9 GB | ~69 | same size |
Specifications
- Publisher
- naver-hyperclovax
- Parameters
- 14.7B
- Architecture
- HyperCLOVAXForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 110,592
- Release Date
- 2025-07-21
- License
- Other
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 20.7 GB (at full 131K context)
VRAM usage by quantization
Can I run HyperCLOVAX SEED Think 14B on a Mac?
Yes — Mac Studio M5 Max (36 GB) and 65 other Macs can run HyperCLOVAX SEED Think 14B. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 30.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run HyperCLOVAX SEED Think 14B locally?
Yes — HyperCLOVAX SEED Think 14B can run locally on consumer hardware. 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)
Real-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. Top options include NVIDIA GeForce RTX 5090.
Which devices can run HyperCLOVAX SEED Think 14B?
78 devices with unified memory can run HyperCLOVAX SEED Think 14B at BF16 (30.1 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.