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

Mistral 7B Instruct v0.3 requires 5.3 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 3.6 GB.

What is the best GPU for Mistral 7B Instruct v0.3?

The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Mistral 7B Instruct v0.3, achieving 380 tok/s at Q8_0 with an excellent rating.

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

For the best quality, use Q5_K_M (5.3 GB VRAM). If your GPU has limited VRAM, Q3_K_M (3.6 GB) is the most efficient option with acceptable quality.

Is Mistral 7B Instruct v0.3 good for coding?

Mistral 7B Instruct v0.3 supports coding use cases. For the best coding experience, pair it with an embedding model for local RAG.

ChatCodingMulti-purpose7.24B

Chat

Mistral 7B Instruct v0.3

Mistral · Apache 2.0

Fast and capable 7B model with sliding window attention. Good all-rounder, slightly behind Llama 3.1 8B on most benchmarks but fully open-source under Apache 2.0.

Parameters: 7.24B
Architecture: Dense
Context: 32,768 tokens
Released: 2024-05-22
Engines: llama.cpp, ollama, vLLM

Parameters

7.24B

VRAM

5.3 GB

Context

32K

Formats

GPUs

Mistral 7B Instruct v0.3 (7.24B) requires 5.3 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 4.5 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 380 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.

Source: OwnRig methodology

VRAM (Recommended)

5.3 GB

Quantization

Q5_K_M

File Size

4.3 GB

Max Context

32K tokens

Primary Use

Chat

Memory

VRAM Requirements

Quality	Quantization	VRAM	File Size
full	Q8_0	8.1 GB	7.2 GB
recommended	Q5_K_M	5.3 GB	4.3 GB
efficient	Q4_K_M	4.5 GB	3.6 GB
compressed	Q3_K_M	3.6 GB	2.8 GB

Scaling

Context Length Impact

KV cache VRAM at Q5_K_M quality. Longer context = more memory.

Context	KV Cache	Total VRAM
2K	102 MB	5.4 GB
4K	205 MB	5.5 GB
8K	410 MB	5.7 GB
16K	922 MB	6.2 GB
32K	1.8 GB	7.1 GB

Compatible GPUs

21 devices


NVIDIA Grace Blackwell Ultra GB300	Q8_0	380 tok/s	Excellent
NVIDIA GeForce RTX 3080 10GB	Q5_K_M	48 tok/s	Excellent
NVIDIA GeForce RTX 4070 Super	Q5_K_M	50 tok/s	Excellent
NVIDIA GeForce RTX 4070 Ti 12GB	Q5_K_M	50 tok/s	Excellent
NVIDIA GeForce RTX 4080 Super	Q8_0	78 tok/s	Excellent
NVIDIA GeForce RTX 4090	Q8_0	90 tok/s	Excellent
AMD Radeon Pro W7900	Q8_0	97 tok/s	Excellent
NVIDIA RTX PRO 6000 Blackwell	Q8_0	136 tok/s	Excellent
NVIDIA RTX PRO 6000 Blackwell Max-Q	Q8_0	125 tok/s	Excellent
NVIDIA GeForce RTX 3060 12GB	Q5_K_M	33 tok/s	Good
NVIDIA GeForce RTX 4060 8GB	Q4_K_M	31 tok/s	Good
NVIDIA RTX 4080 Laptop (120-150W)	Q5_K_M	35 tok/s	Good
AMD Radeon RX 7900 XTX	Q8_0	77 tok/s	Good
NVIDIA GeForce RTX 5060 8GB	Q4_K_M	36 tok/s	Good
Apple M3 Pro (18GB Unified)	Q4_K_M	14 tok/s	Acceptable
NVIDIA RTX 4060 Laptop (40-60W)	Q4_K_M	19 tok/s	Acceptable
NVIDIA RTX 4070 Laptop (80-115W)	Q4_K_M	22 tok/s	Acceptable
AMD Radeon RX 7600	Q4_K_M	24 tok/s	Acceptable
AMD Radeon RX 9070	Q8_0	–	Acceptable
AMD Radeon RX 9060 XT 16GB	Q8_0	–	Acceptable
AMD Radeon RX 9060 XT 8GB	Q8_0	–	Not viable

Showing 21 of 21 entries

Hardware

Recommended Builds

Complete PC builds that can run Mistral 7B Instruct v0.3.

Budget

Budget AI Desktop

Your own AI coding setup for under $800

RTX 3060 12GB·12 GBVRAM

Runs 7 models

$684

Budget

Budget Home AI Server

Always-on AI assistant for the whole household

RTX 4060 Ti 16GB·16 GBVRAM

Runs 7 models

$1,063

Mid-range

Compact SFF AI Build

Serious AI power in a compact, desk-friendly form factor

RTX 4070 Super 12GB·12 GBVRAM

Runs 5 models

$1,304

Mid-range

Mid-Range Home AI Server

Serve multiple AI models to every device at home

RTX 3090 24GB (Used)·24 GBVRAM

Runs 9 models

$1,773

Mid-range

Silent Mini-ITX AI Box

Whisper-quiet AI processing for noise-sensitive environments

RTX 4060 Ti 16GB·16 GBVRAM

Runs 8 models

$1,114

Budget

Starter AI Desktop

Run your first local AI models for under $600

RTX 3060 12GB·12 GBVRAM

Runs 6 models

$543

FAQ

Frequently Asked Questions

How much VRAM does Mistral 7B Instruct v0.3 need?: Mistral 7B Instruct v0.3 requires 5.3 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 3.6 GB.
What is the best GPU for Mistral 7B Instruct v0.3?: The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Mistral 7B Instruct v0.3, achieving 380 tok/s at Q8_0 with an excellent rating.
What quantization should I use for Mistral 7B Instruct v0.3?: For the best quality, use Q5_K_M (5.3 GB VRAM). If your GPU has limited VRAM, Q3_K_M (3.6 GB) is the most efficient option with acceptable quality.
Is Mistral 7B Instruct v0.3 good for coding?: Mistral 7B Instruct v0.3 supports coding use cases. For the best coding experience, pair it with an embedding model for local RAG.

Related Guides

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Explainer

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Explainer

How we test: OwnRig's benchmark methodology

How OwnRig measures tokens per second, rates model compatibility, and keeps hardware data current. Our methodology, tools, and known limitations.

All models

Data confidence: verified. Source

VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Mistral is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.