Can InternVL2 8B run on MacBook Air M1 16GB?

Q: Is unified memory on MacBook Air M1 16GB as fast as VRAM for InternVL2 8B?

Not always. MacBook Air M1 16GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

YES — Tight Fit

A79Great

Estimated from fit model

InternVL2 8B needs ~9.5 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.5 GB, 9.0 tok/s, Tight fit

9.5 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

9.0 tok/s

TTFT

21541 ms

Safe context

Memory

9.5 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsInternVL2 8B on MacBook Air M1 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 9.0 tok/s decode · 21.5s TTFT (warm) · 23 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	9.0 tok/s	11749 ms	8K
Coding	A	Tight fit	9.0 tok/s	21541 ms	8K
Agentic Coding	A	Runs with offload	9.0 tok/s	31332 ms	8K
Reasoning	A	Tight fit	9.0 tok/s	25457 ms	8K
RAG	A	Runs with offload	9.0 tok/s	39165 ms	8K

Quantization options

How InternVL2 8B (8B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A82
Q3_K_S	3	3.9 GB	Low	A83
NVFP4	4

Get started

Copy-paste commands to run InternVL2 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "OpenGVLab/InternVL2-8B" \
  --hf-file "InternVL2-8B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Air M1 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	8 tok/s
Qwen 3 14B	14B	B	4 tok/s

Frequently asked questions

Can MacBook Air M1 16GB run InternVL2 8B?

Yes, MacBook Air M1 16GB can run InternVL2 8B with a A grade (Tight fit). Expected decode speed: 9.0 tok/s.

How much VRAM does InternVL2 8B need?

InternVL2 8B (8B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for InternVL2 8B?

The recommended quantization for InternVL2 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will InternVL2 8B run at on MacBook Air M1 16GB?

On MacBook Air M1 16GB, InternVL2 8B achieves approximately 9.0 tokens per second decode speed with a time-to-first-token of 21541ms using Q4_K_M quantization.

Can MacBook Air M1 16GB run InternVL2 8B for coding?

For coding workloads, InternVL2 8B on MacBook Air M1 16GB receives a A grade with 9.0 tok/s and 8K context.

What context window can InternVL2 8B use on MacBook Air M1 16GB?

On MacBook Air M1 16GB, InternVL2 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M1 16GB as fast as VRAM for InternVL2 8B?

See all results for MacBook Air M1 16GB See all hardware for InternVL2 8B

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