Marin 8B Instruct — Hardware Requirements & GPU Compatibility
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Marin 8B Instruct is a 8.0B-parameter open language model from marin-community. It supports a context window of up to 4,096 tokens. At BF16 it needs about 16.63 GB of VRAM — see which GPUs and Macs can run it below.
2.0K downloads
28 likesSpecifications
- Publisher
- marin-community
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-05-19
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Marin 8B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 16.6 GB | 16.9 GB | 16.06 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Marin 8B Instruct?
BF16 · 16.6 GBMarin 8B Instruct (BF16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 4K context window can add up to 0.3 GB, bringing total usage to 16.9 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Marin 8B Instruct?
BF16 · 16.6 GB21 devices with unified memory can run Marin 8B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Marin 8B Instruct need?
Marin 8B Instruct requires 16.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 16 bits ÷ 8 = 16.1 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.8 GB (at full 4K context)
VRAM usage by quantization
BF1616.6 GBBF16 + full context16.9 GB- Can I run Marin 8B Instruct on a Mac?
Marin 8B Instruct requires at least 16.6 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 Marin 8B Instruct locally?
Yes — Marin 8B Instruct can run locally on consumer hardware. At BF16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Marin 8B Instruct?
At BF16, Marin 8B Instruct can reach ~175 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 16.6 × 0.55 = ~175 tok/s
Estimated speed at BF16 (16.6 GB)
~175 tok/s~39 tok/s~131 tok/s~108 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Marin 8B Instruct?
At BF16, the download is about 16.06 GB.
- Which GPUs can run Marin 8B Instruct?
6 consumer GPUs can run Marin 8B Instruct at BF16 (16.6 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 Marin 8B Instruct?
21 devices with unified memory can run Marin 8B Instruct at BF16 (16.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.