Minerva 7B Instruct v1.0 — Hardware Requirements & GPU Compatibility
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Minerva 7B Instruct v1.0 is a 7B-parameter open language model from sapienzanlp. It supports a context window of up to 4,096 tokens. At BF16 it needs about 14.57 GB of VRAM — see which GPUs and Macs can run it below.
2.8K downloads
23 likesSpecifications
- Publisher
- sapienzanlp
- Parameters
- 7B
- Architecture
- MistralForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 51,264
- Release Date
- 2024-12-05
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Minerva 7B Instruct v1.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 14.6 GB | 14.8 GB | 14.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Minerva 7B Instruct v1.0?
BF16 · 14.6 GBMinerva 7B Instruct v1.0 (BF16) requires 14.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 19+ GB is recommended. Using the full 4K context window can add up to 0.3 GB, bringing total usage to 14.8 GB. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Minerva 7B Instruct v1.0?
BF16 · 14.6 GB27 devices with unified memory can run Minerva 7B Instruct v1.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Minerva 7B Instruct v1.0 need?
Minerva 7B Instruct v1.0 requires 14.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 16 bits ÷ 8 = 14 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.8 GB (at full 4K context)
VRAM usage by quantization
BF1614.6 GBBF16 + full context14.8 GB- Can I run Minerva 7B Instruct v1.0 on a Mac?
Minerva 7B Instruct v1.0 requires at least 14.6 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 Minerva 7B Instruct v1.0 locally?
Yes — Minerva 7B Instruct v1.0 can run locally on consumer hardware. At BF16 quantization it needs 14.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Minerva 7B Instruct v1.0?
At BF16, Minerva 7B Instruct v1.0 can reach ~200 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~45 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 ÷ 14.6 × 0.55 = ~200 tok/s
Estimated speed at BF16 (14.6 GB)
~200 tok/s~45 tok/s~150 tok/s~124 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Minerva 7B Instruct v1.0?
At BF16, the download is about 14.00 GB.
- Which GPUs can run Minerva 7B Instruct v1.0?
17 consumer GPUs can run Minerva 7B Instruct v1.0 at BF16 (14.6 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Minerva 7B Instruct v1.0?
27 devices with unified memory can run Minerva 7B Instruct v1.0 at BF16 (14.6 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.