Mistral 7B on Apple Silicon

One of the most downloaded local LLMs. Fast, capable, and comfortable on any Mac with 16 GB or more.

Mistral 7B was released in 2023 and quickly became one of the most popular base models for local inference. It outperforms Llama 2 7B on most benchmarks while using similar compute — and at Q4_K_M, it runs at 30–55+ tok/s depending on your chip. Mistral uses a sliding window attention and grouped-query attention, which makes it fast on bandwidth-limited hardware like Apple Silicon.

~4.1 GB RAM at Q4_K_M

~7.7 GB RAM at Q8_0

16 GB+ minimum Mac for comfortable use

30–55+ tok/s range depending on chip

Mistral 7B speed estimates by chip

Mistral 7B uses the same basic transformer architecture as Llama 2 7B and Llama 3 8B. Speed should be comparable to Llama 3.1 8B benchmarks. Where Llama 3.1 8B data exists in our dataset, we use those directly as a proxy.

SiliconBench does not yet have first-party Mistral 7B benchmark data. Estimates below are based on Llama 3.1 8B Instruct measurements — Mistral 7B typically runs 5–10% faster due to smaller vocabulary and architecture differences.

Chip	RAM	Llama 3.1 8B (measured)	Mistral 7B (estimated)	Source
M4 Max (40-core GPU, 64 GB)	64 GB	55.1 tok/s	~58–62 tok/s	estimated
M4 Max (40-core GPU, 48 GB)	48 GB	55.1 tok/s	~58–62 tok/s	estimated
M4 Pro (20-core GPU, 24 GB)	24 GB	32.5 tok/s	~34–37 tok/s	estimated
M3 Max (40-core GPU, 48 GB)	48 GB	37.5 tok/s	~39–43 tok/s	estimated
M3 Max (30-core GPU, 36 GB)	36 GB	37.5 tok/s	~39–43 tok/s	estimated
M3 Pro (18-core GPU, 36 GB)	36 GB	22.1 tok/s	~23–26 tok/s	estimated
M2 Ultra (60-core GPU, 64 GB)	64 GB	59.5 tok/s	~62–68 tok/s	estimated
M1 Pro (14-core GPU)	16–32 GB	~25–28 tok/s	~26–30 tok/s	estimated

See the full Llama 3.1 8B benchmark data → for measured chip-by-chip comparisons at Q4_K_M.

Mistral 7B vs Llama 3.1 8B

Both are the workhorses of local inference. Key differences:

Feature	Mistral 7B	Llama 3.1 8B Instruct
Parameters	7.2B	8.0B
Context window	32K (with sliding window)	128K
RAM at Q4_K_M	~4.1 GB	~4.7 GB
Speed (relative)	Slightly faster	Baseline
Instruction following	Good (Instruct variant)	Excellent
Code quality	Good	Better (trained on more code)
Ollama model tag	`mistral`	`llama3.1`

Llama 3.1 8B is generally the better choice for new deployments.

Llama 3.1 8B has a 128K context window vs Mistral 7B's 32K (with sliding window), significantly better instruction following, and comparable or better performance on most benchmarks. Mistral 7B remains a solid choice for simple chat and generation tasks, and benefits from a large ecosystem of fine-tunes. For most new setups, Llama 3.1 8B is the recommended 7B-class starting point.

Running Mistral 7B with Ollama

# Install Ollama (if not installed)
# Download from https://ollama.ai

# Run Mistral 7B (auto-selects Q4_K_M)
ollama run mistral

# Run Mistral 7B Instruct (better for conversation)
ollama run mistral:instruct

# Run Mixtral 8x7B (MoE — needs 26+ GB RAM)
ollama run mixtral

Related model benchmarks

Llama 3.1 8B benchmarks Llama 3.2 1B benchmarks Qwen 2.5 14B benchmarks DeepSeek R1 on Apple Silicon Llama vs Qwen comparison RAM Calculator Best Mac for local LLMs

Data

benchmarks.json — full dataset · chips.json — chip summaries · benchmarks.csv — CSV export

See all chips →