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Can the Apple M5 (16GB) run Mistral 7B v0.3?

Apple M5 (16GB) × Mistral 7B v0.38K context
YesRuns fully on the GPU
Memory at Q8_09.4 GB of 10.7 GB
Weights 7.7 GBKV cache 1.1 GBRuntime 0.6 GB
QuantQ8_0Near-lossless
Speed~12tokens per second
Max context17Ktokens that fit at Q8_0
  1. FP16
  2. Q8_0
  3. Q6_K
  4. Q5_K_M
  5. Q4_K_M
  6. Q3_K_M
  7. Q2_K

Yes. The Apple M5 (16GB) can run Mistral 7B v0.3 fully in unified memory at Q8_0 (near-lossless), using about 9.4 GB of the 10.7 GB available, at roughly 12 tokens/second.

Try other context lengths

Run it with Ollama

With Ollama installed, one command downloads and starts Mistral 7B v0.3:

ollama run mistral:7b

Ollama's default download is Q4_K_M (about 6.1 GB here). You have room for Q8_0, which is near-lossless: pick that GGUF in LM Studio or from Hugging Face.

New to this? Step-by-step setup for the Apple M5 (16GB)

Run it in the cloud

No Apple M5 (16GB)? Run Mistral 7B v0.3 in the cloud.

Rent a cloud GPU by the hour. No upfront cost, stop any time.

Rent a GPU on RunPodRent a GPU on Vast.ai

Memory needed by quantization

At 8,192 tokens of context. The black line marks the 10.7 GB the Apple M5 (16GB) can use. macOS lets the GPU use about 10.7 GB of the 16 GB unified memory by default.

QuantQualityMemoryFits?Speed
FP16Full quality16.2 GBToo big—
Q8_0Near-lossless9.4 GBFits~12 tok/s
Q6_KExcellent7.6 GBFits~15 tok/s
Q5_K_MVery good6.8 GBFits~18 tok/s
Q4_K_MGood, most popular6.1 GBFits~21 tok/s
Q3_K_MNoticeably degraded5.2 GBFits~26 tok/s
Q2_KHeavily degraded4.4 GBFits~34 tok/s

Measured speed from real Apple M5 (16GB) owners

Tokens per second that people measured on their own Apple M5 (16GB), next to our estimate.

No one has measured Mistral 7B v0.3 on the Apple M5 (16GB) yet. The speeds above are estimates. Run the benchmark and yours becomes the first real number on this page.

Have an Apple M5 (16GB)? Add your real speed

One command times Mistral 7B v0.3 in your own Ollama, shows you exactly what it would send, and asks before sending anything. No sign-up, no personal data.

macOS and Linux

curl -fsSL https://gpufit.dev/bench.sh | sh -s -- mistral:7b

Windows (PowerShell)

$env:GPUFIT_MODEL="mistral:7b"; irm https://gpufit.dev/bench.ps1 | iex

How the benchmark works

GPUs that run Mistral 7B v0.3 comfortably

Models that run well on the Apple M5 (16GB)

Frequently asked questions

Can Apple M5 (16GB) run Mistral 7B v0.3?

Yes. The Apple M5 (16GB) can run Mistral 7B v0.3 fully in unified memory at Q8_0 (near-lossless), using about 9.4 GB of the 10.7 GB available, at roughly 12 tokens/second.

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

About 6.1 GB at Q4_K_M with 8,192 tokens of context, and about 16.2 GB at full FP16 precision.

How fast is Mistral 7B v0.3 on the Apple M5 (16GB)?

Roughly 12 tokens per second at Q8_0, estimated from the Apple M5 (16GB)'s 153 GB/s memory bandwidth.