Can InternVL2 8B run on Mac Studio M3 Ultra 256GB?

Q: Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for InternVL2 8B?

Not always. Mac Studio M3 Ultra 256GB 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 — Runs Great

A78Great

Estimated from fit model

InternVL2 8B needs ~35.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 35.4 GB, 112.0 tok/s, Runs well

35.4 GB required184.3 GB available

19% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

Memory

35.4 GB / 184.3 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsInternVL2 8B on Mac Studio M3 Ultra 256GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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	112.0 tok/s	943 ms	8K
Coding	A	Runs well	112.0 tok/s	1729 ms	8K
Agentic Coding	A	Runs well	112.0 tok/s	2514 ms	8K
Reasoning	A	Runs well	112.0 tok/s	2043 ms	8K
RAG	A	Runs well	112.0 tok/s	3143 ms	8K

Inference speed

InternVL2 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for InternVL2 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0

Quantization options

How InternVL2 8B (8B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B69
Q3_K_S	3	3.9 GB	Low	B69
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 Mac Studio M3 Ultra 256GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	8.1 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run InternVL2 8B?

Yes, Mac Studio M3 Ultra 256GB can run InternVL2 8B with a A grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does InternVL2 8B need?

InternVL2 8B (8B parameters) requires approximately 35.4 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 Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, InternVL2 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run InternVL2 8B for coding?

For coding workloads, InternVL2 8B on Mac Studio M3 Ultra 256GB receives a A grade with 112.0 tok/s and 8K context.

What context window can InternVL2 8B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, 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 Mac Studio M3 Ultra 256GB as fast as VRAM for InternVL2 8B?

See all results for Mac Studio M3 Ultra 256GB See all hardware for InternVL2 8B

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