Mistral 7B Instruct v0.3 GGUF — Hardware Requirements & GPU Compatibility
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122.6K downloads
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- Publisher
- MaziyarPanahi
- Family
- Mistral
- Parameters
- 7B
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HuggingFace
How Much VRAM Does Mistral 7B Instruct v0.3 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XS | 2.40 | 2.3 GB | — | 2.10 GB | Importance-weighted 2-bit, extra small |
| IQ3_XS | 3.30 | 3.2 GB | — | 2.89 GB | Importance-weighted 3-bit, extra small |
| 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 |
| Q3_K_L | 4.10 | 4.0 GB | — | 3.59 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.1 GB | — | 3.76 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 4.3 GB | — | 3.94 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 4.6 GB | — | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 5.3 GB | — | 4.81 GB | 5-bit small quantization |
| 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 Instruct v0.3 GGUF?
Q4_K_M · 4.6 GBMistral 7B Instruct v0.3 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 Instruct v0.3 GGUF?
Q4_K_M · 4.6 GB33 devices with unified memory can run Mistral 7B Instruct v0.3 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Mistral 7B Instruct v0.3 GGUF need?
Mistral 7B Instruct v0.3 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 Instruct v0.3 GGUF?
For Mistral 7B Instruct v0.3 GGUF, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 2.3 GB.
VRAM requirement by quantization
IQ2_XS2.3 GB~57%Q3_K_S3.4 GB~77%IQ4_XS4.1 GB~87%Q4_K_M ★4.6 GB~89%Q5_K_S5.3 GB~92%Q8_07.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral 7B Instruct v0.3 GGUF on a Mac?
Mistral 7B Instruct v0.3 GGUF requires at least 2.3 GB at IQ2_XS, 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 v0.3 GGUF locally?
Yes — Mistral 7B Instruct v0.3 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 Instruct v0.3 GGUF?
At Q4_K_M, Mistral 7B Instruct v0.3 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 Instruct v0.3 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 (IQ2_XS) is 2.10 GB.