StripedHyena Nous 7B — Hardware Requirements & GPU Compatibility
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StripedHyena Nous 7B is a 7.6B-parameter open language model from togethercomputer. At BF16 it needs about 16.82 GB of VRAM — see which GPUs and Macs can run it below.
185 downloads
145 likesSpecifications
- Publisher
- togethercomputer
- Parameters
- 7.6B
- Architecture
- StripedHyenaModelForCausalLM
- Vocabulary Size
- 32,000
- Release Date
- 2023-12-04
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does StripedHyena Nous 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 16.8 GB | — | 15.29 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 StripedHyena Nous 7B?
BF16 · 16.8 GBStripedHyena Nous 7B (BF16) requires 16.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run StripedHyena Nous 7B?
BF16 · 16.8 GB41 devices with unified memory can run StripedHyena Nous 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does StripedHyena Nous 7B need?
StripedHyena Nous 7B requires 16.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 16 bits ÷ 8 = 15.3 GB
KV Cache + Overhead ≈ 1.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1616.8 GB- Can I run StripedHyena Nous 7B on a Mac?
StripedHyena Nous 7B requires at least 16.8 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 StripedHyena Nous 7B locally?
Yes — StripedHyena Nous 7B can run locally on consumer hardware. At BF16 quantization it needs 16.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is StripedHyena Nous 7B?
At BF16, StripedHyena Nous 7B can reach ~285 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~39 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 ÷ 16.8 × 0.65 = ~309 tok/s
Estimated speed at BF16 (16.8 GB)
~309 tok/s~39 tok/s~309 tok/s~285 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of StripedHyena Nous 7B?
At BF16, the download is about 15.29 GB.
- Which GPUs can run StripedHyena Nous 7B?
8 consumer GPUs can run StripedHyena Nous 7B at BF16 (16.8 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run StripedHyena Nous 7B?
41 devices with unified memory can run StripedHyena Nous 7B at BF16 (16.8 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.