Mistral 7B v0.1 — Hardware Requirements & GPU Compatibility
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Mistral 7B v0.1 is the original base model from Mistral AI that helped reshape expectations for small open-weight language models when it launched in late 2023. As a pretrained foundation model without instruction tuning, it is designed for fine-tuning, research, and custom downstream tasks rather than direct conversational use. With 7 billion parameters and support for grouped-query attention and sliding-window attention, it remains a popular starting point for practitioners building specialized models. Its modest VRAM requirements of roughly 6 GB at 4-bit quantization keep it accessible on a wide range of consumer GPUs.
539.9K downloads
4.1K likesSpecifications
- Publisher
- Mistral AI
- Family
- Mistral
- Parameters
- 7B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2025-07-24
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Mistral 7B v0.1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_XS | 3.30 | 3.5 GB | 7.5 GB | 2.89 GB | Importance-weighted 3-bit, extra small |
| IQ3_S | 3.40 | 3.5 GB | 7.6 GB | 2.98 GB | Importance-weighted 3-bit, small |
| Q2_K | 3.40 | 3.5 GB | 7.6 GB | 2.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.6 GB | 7.7 GB | 3.06 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 3.7 GB | 7.7 GB | 3.15 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 4.0 GB | 8.0 GB | 3.41 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.1 GB | 8.1 GB | 3.50 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 4.2 GB | 8.2 GB | 3.59 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.3 GB | 8.4 GB | 3.76 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 4.5 GB | 8.5 GB | 3.94 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 4.8 GB | 8.8 GB | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 4.9 GB | 9.0 GB | 4.38 GB | 5-bit legacy quantization |
| Q5_K_S | 5.50 | 5.4 GB | 9.4 GB | 4.81 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 5.6 GB | 9.6 GB | 4.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.3 GB | 10.4 GB | 5.78 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.6 GB | 11.6 GB | 7.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mistral 7B v0.1?
Q4_K_M · 4.8 GBMistral 7B v0.1 (Q4_K_M) requires 4.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 4.0 GB, bringing total usage to 8.8 GB. 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?
Q4_K_M · 4.8 GB33 devices with unified memory can run Mistral 7B v0.1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (9)
Frequently Asked Questions
- How much VRAM does Mistral 7B v0.1 need?
Mistral 7B v0.1 requires 4.8 GB of VRAM at Q4_K_M, or 7.6 GB at Q8_0. Full 33K context adds up to 4.0 GB (8.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 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
Q4_K_M4.8 GBQ4_K_M + full context8.8 GB- What's the best quantization for Mistral 7B v0.1?
For Mistral 7B v0.1, Q4_K_M (4.8 GB) offers the best balance of quality and VRAM usage. Q5_0 (4.9 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 3.5 GB.
VRAM requirement by quantization
IQ3_XS3.5 GB~73%IQ3_M3.7 GB~78%IQ4_XS4.3 GB~87%Q4_K_M ★4.8 GB~89%Q5_04.9 GB~90%Q8_07.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral 7B v0.1 on a Mac?
Mistral 7B v0.1 requires at least 3.5 GB at IQ3_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 v0.1 locally?
Yes — Mistral 7B v0.1 can run locally on consumer hardware. At Q4_K_M quantization it needs 4.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral 7B v0.1?
At Q4_K_M, Mistral 7B v0.1 can reach ~611 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~137 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 ÷ 4.8 × 0.55 = ~611 tok/s
Estimated speed at Q4_K_M (4.8 GB)
AMD Instinct MI300X~611 tok/sNVIDIA GeForce RTX 4090~137 tok/sNVIDIA H100 SXM~457 tok/sAMD Instinct MI250X~378 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?
At Q4_K_M, the download is about 4.20 GB. The full-precision Q8_0 version is 7.00 GB. The smallest option (IQ3_XS) is 2.89 GB.