HyperLLM 4B — Hardware Requirements & GPU Compatibility
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HyperLLM 4B is a 4B-parameter open language model from UVLabs. At BF16 it needs about 8.80 GB of VRAM — see which GPUs and Macs can run it below.
91 downloads
3 likesSpecifications
- Publisher
- UVLabs
- Parameters
- 4B
- Release Date
- 2026-03-03
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does HyperLLM 4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 8.8 GB | — | 8.00 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 HyperLLM 4B?
BF16 · 8.8 GBHyperLLM 4B (BF16) requires 8.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. 39 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run HyperLLM 4B?
BF16 · 8.8 GB49 devices with unified memory can run HyperLLM 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does HyperLLM 4B need?
HyperLLM 4B requires 8.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 16 bits ÷ 8 = 8 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF168.8 GB- Can I run HyperLLM 4B on a Mac?
HyperLLM 4B requires at least 8.8 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 HyperLLM 4B locally?
Yes — HyperLLM 4B can run locally on consumer hardware. At BF16 quantization it needs 8.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is HyperLLM 4B?
At BF16, HyperLLM 4B can reach ~500 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~75 tok/s. 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 ÷ 8.8 × 0.65 = ~591 tok/s
Estimated speed at BF16 (8.8 GB)
~591 tok/s~75 tok/s~591 tok/s~500 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of HyperLLM 4B?
At BF16, the download is about 8.00 GB.
- Which GPUs can run HyperLLM 4B?
39 consumer GPUs can run HyperLLM 4B at BF16 (8.8 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run HyperLLM 4B?
52 devices with unified memory can run HyperLLM 4B at BF16 (8.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.