Mistral 7B Instruct v0.1 GGUF — Hardware Requirements & GPU Compatibility
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112 downloads
2 likesSpecifications
- Publisher
- second-state
- Family
- Mistral
- Parameters
- 7B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,000
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Mistral 7B Instruct v0.1 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 14.6 GB | 18.6 GB | 14.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Mistral 7B Instruct v0.1 GGUF?
BF16 · 14.6 GBMistral 7B Instruct v0.1 GGUF (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 33K context window can add up to 4.0 GB, bringing total usage to 18.6 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 Mistral 7B Instruct v0.1 GGUF?
BF16 · 14.6 GB27 devices with unified memory can run Mistral 7B Instruct v0.1 GGUF, 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 Mistral 7B Instruct v0.1 GGUF need?
Mistral 7B Instruct v0.1 GGUF requires 14.6 GB of VRAM at BF16. Full 33K context adds up to 4.0 GB (18.6 GB total).
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 ≈ 4.6 GB (at full 33K context)
VRAM usage by quantization
BF1614.6 GBBF16 + full context18.6 GB- Can I run Mistral 7B Instruct v0.1 GGUF on a Mac?
Mistral 7B Instruct v0.1 GGUF 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 Mistral 7B Instruct v0.1 GGUF locally?
Yes — Mistral 7B Instruct v0.1 GGUF 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 Mistral 7B Instruct v0.1 GGUF?
At BF16, Mistral 7B Instruct v0.1 GGUF 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)
AMD Instinct MI300X~200 tok/sNVIDIA GeForce RTX 4090~45 tok/sNVIDIA H100 SXM~150 tok/sAMD Instinct MI250X~124 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mistral 7B Instruct v0.1 GGUF?
At BF16, the download is about 14.00 GB.
- Which GPUs can run Mistral 7B Instruct v0.1 GGUF?
17 consumer GPUs can run Mistral 7B Instruct v0.1 GGUF 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 Mistral 7B Instruct v0.1 GGUF?
27 devices with unified memory can run Mistral 7B Instruct v0.1 GGUF 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.