How much VRAM does Mistral 7B v0.3 need?

Mistral 7B v0.3 requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Mistral 7B v0.3 on a consumer GPU?

Yes, Mistral 7B v0.3 can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 12 GB GPU. For best performance, use a 12 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Mistral 7B v0.3's context window?

Mistral 7B v0.3 supports a maximum context window of 32,768 tokens. The default context is 8,192 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Mistral 7B v0.3?

For Mistral 7B v0.3, Q4_K_M is the recommended quantization — it provides an excellent quality-to-VRAM ratio at ~0.0 GB. Q8_0 offers slightly better quality at ~2× the VRAM. Q4_K_M is the sweet spot for most users.

How does Mistral 7B v0.3 compare to similar models?

Mistral 7B v0.3 is a 7.3B Dense model from the Mistral family. Browse other Mistral models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

MistralDenseApache 2.0

Mistral 7B v0.3

7.3B

Parameters

32K

Max Context

Dense

Architecture

May 20, 2024

Released

Text

Modality

About Mistral 7B v0.3

Mistral 7B v0.3 is the refined version of the model that launched the European open-source LLM movement. At 7.25B parameters, it punches above its weight on reasoning and multilingual tasks, particularly in French, German, Spanish, and Italian. Apache 2.0 licensing makes it a safe choice for commercial deployment. With mature GGUF support and ~4 GB VRAM at Q4_K_M, it remains a solid alternative to Llama 3.1 8B — especially for multilingual use cases.

MultilingualChatCodeCommercial

Technical Specifications

Total Parameters7.3B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 4,096

Transformer Layers32

Attention Heads32

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	32K ctx
Q4_K_M0.50 B/W ~97% of FP16	3.87Consumer GPU	7.75Consumer GPU
Q8_01.00 B/W ~100% of FP16	7.62Consumer GPU	11.49Consumer GPU
F162.00 B/W Reference	15.11Consumer GPU	18.99Consumer GPU

Params

675.0B

Layers

Context

256K

41.0B active

Find the right GPU for Mistral 7B v0.3

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide