Mistral 7B v0.1 GGUF — Hardware Requirements & GPU Compatibility
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7.7K downloads
274 likesSpecifications
- Publisher
- TheBloke
- Family
- Mistral
- Parameters
- 7B
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Mistral 7B v0.1 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.3 GB | — | 2.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.4 GB | — | 3.06 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.8 GB | — | 3.41 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.9 GB | — | 3.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.6 GB | — | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.5 GB | — | 4.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.3 GB | — | 5.78 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.7 GB | — | 7.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mistral 7B v0.1 GGUF?
Q4_K_M · 4.6 GBMistral 7B v0.1 GGUF (Q4_K_M) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Mistral 7B v0.1 GGUF?
Q4_K_M · 4.6 GB33 devices with unified memory can run Mistral 7B v0.1 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Mistral 7B v0.1 GGUF need?
Mistral 7B v0.1 GGUF requires 4.6 GB of VRAM at Q4_K_M, or 7.7 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.6 GB- What's the best quantization for Mistral 7B v0.1 GGUF?
For Mistral 7B v0.1 GGUF, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (4.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
Q2_K3.3 GB~75%Q4_03.9 GB~85%Q4_K_M ★4.6 GB~89%Q5_04.8 GB~90%Q5_K_S5.3 GB~92%Q8_07.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral 7B v0.1 GGUF on a Mac?
Mistral 7B v0.1 GGUF requires at least 3.3 GB at Q2_K, 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 v0.1 GGUF locally?
Yes — Mistral 7B v0.1 GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 4.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral 7B v0.1 GGUF?
At Q4_K_M, Mistral 7B v0.1 GGUF can reach ~631 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~142 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 ÷ 4.6 × 0.55 = ~631 tok/s
Estimated speed at Q4_K_M (4.6 GB)
AMD Instinct MI300X~631 tok/sNVIDIA GeForce RTX 4090~142 tok/sNVIDIA H100 SXM~472 tok/sAMD Instinct MI250X~390 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 v0.1 GGUF?
At Q4_K_M, the download is about 4.20 GB. The full-precision Q8_0 version is 7.00 GB. The smallest option (Q2_K) is 2.98 GB.
- Which GPUs can run Mistral 7B v0.1 GGUF?
35 consumer GPUs can run Mistral 7B v0.1 GGUF at Q4_K_M (4.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mistral 7B v0.1 GGUF?
33 devices with unified memory can run Mistral 7B v0.1 GGUF at Q4_K_M (4.6 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.