Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for TinyLlama 1.1B Chat v0.6?

Not always. MacBook Pro M4 Max 128GB 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.

Can TinyLlama 1.1B Chat v0.6 run on MacBook Pro M4 Max 128GB?

YES — Runs Great

D40Poor

Estimated from fit model

TinyLlama 1.1B Chat v0.6 needs ~15.5 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 15.5 GB, 15.4 tok/s, Runs well

15.5 GB required92.2 GB available

17% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12571 ms

Safe context

9.5M

Memory

15.5 GB / 92.2 GB

Memory breakdown

Weights0.7 GB

KV Cache0.1 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsTinyLlama 1.1B Chat v0.6 on MacBook Pro M4 Max 128GB
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: 15.4 tok/s decode · 12.6s TTFT (warm) · 39 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	D	Runs well	15.4 tok/s	6857 ms	6.1M
Coding	D	Runs well	15.4 tok/s	12571 ms	9.5M
Agentic Coding	D	Runs well	15.4 tok/s	18286 ms	9.5M
Reasoning	D	Runs well	15.4 tok/s	14857 ms	9.5M
RAG	D	Runs well	15.4 tok/s	22857 ms	9.5M

Inference speed

TinyLlama 1.1B Chat v0.6 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for TinyLlama 1.1B Chat v0.6 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~21 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	20.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	17.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	17.6

Quantization options

How TinyLlama 1.1B Chat v0.6 (1.100000023841858B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	D39
Q3_K_S	3	0.5 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run TinyLlama 1.1B Chat v0.6 on your machine.

Run

lms load hf-tinyllama--tinyllama-1-1b-chat-v0-6 && lms server start

Frequently asked questions

Can MacBook Pro M4 Max 128GB run TinyLlama 1.1B Chat v0.6?

Yes, MacBook Pro M4 Max 128GB can run TinyLlama 1.1B Chat v0.6 with a D grade (Runs well). Expected decode speed: 15.4 tok/s.

How much VRAM does TinyLlama 1.1B Chat v0.6 need?

TinyLlama 1.1B Chat v0.6 (1.100000023841858B parameters) requires approximately 15.5 GB of memory with Q4_K_M quantization.

What is the best quantization for TinyLlama 1.1B Chat v0.6?

The recommended quantization for TinyLlama 1.1B Chat v0.6 is Q4_K_M, which balances quality and memory efficiency.

What speed will TinyLlama 1.1B Chat v0.6 run at on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, TinyLlama 1.1B Chat v0.6 achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 128GB run TinyLlama 1.1B Chat v0.6 for coding?

For coding workloads, TinyLlama 1.1B Chat v0.6 on MacBook Pro M4 Max 128GB receives a D grade with 15.4 tok/s and 9.5M context.

What context window can TinyLlama 1.1B Chat v0.6 use on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, TinyLlama 1.1B Chat v0.6 can safely use up to 9.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 128GB See all hardware for TinyLlama 1.1B Chat v0.6

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