Will It Run AI

Can Llama 3 8B Instruct 32k v0.1 run on MacBook Pro M4 Max 36GB?

YES — Runs Great

C49Usable
Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~10.6 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:

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) — 10.6 GB, 57.7 tok/s, Runs well
10.6 GB required25.9 GB available
41% VRAM used

Fit status

Runs well

Decode

57.7 tok/s

TTFT

3356 ms

Safe context

277K

Memory

10.6 GB / 25.9 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom3.9 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on MacBook Pro M4 Max 36GB
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: 57.7 tok/s decode · 3.4s TTFT (warm) · 144 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well52.9 tok/s1995 ms277K
CodingCRuns well52.9 tok/s3658 ms277K
Agentic CodingCRuns well52.9 tok/s5320 ms277K
ReasoningCRuns well52.9 tok/s4323 ms277K
RAGCRuns well52.9 tok/s6650 ms277K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC44
Q3_K_S
3
3.9 GB
LowC45
NVFP4
4
4.5 GB
MediumC45
Q4_K_M
4
4.9 GB
MediumC45
Q5_K_M
5
5.8 GB
HighC46
Q6_K
6
6.6 GB
HighC46
Q8_0
8
8.6 GB
Very HighC47
F16Best for your GPU
16
16.4 GB
MaximumC50

Get started

Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.

Run

lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server start

Frequently asked questions

Can MacBook Pro M4 Max 36GB run Llama 3 8B Instruct 32k v0.1?

Yes, MacBook Pro M4 Max 36GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 52.9 tok/s.

How much VRAM does Llama 3 8B Instruct 32k v0.1 need?

Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3 8B Instruct 32k v0.1?

The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3 8B Instruct 32k v0.1 run at on MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 52.9 tokens per second decode speed with a time-to-first-token of 3658ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 36GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on MacBook Pro M4 Max 36GB receives a C grade with 52.9 tok/s and 277K context.

What context window can Llama 3 8B Instruct 32k v0.1 use on MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 277K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 36GB as fast as VRAM for Llama 3 8B Instruct 32k v0.1?

Not always. MacBook Pro M4 Max 36GB 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.

See all results for MacBook Pro M4 Max 36GBSee all hardware for Llama 3 8B Instruct 32k v0.1
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-m4-max-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: