Will It Run AI

Can CodeLlama 13B Instruct run on MacBook Pro M1 Pro 32GB?

YES — With Offload

B63Good
Estimated from fit model

CodeLlama 13B Instruct needs ~24.5 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
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) — 24.5 GB, 14.7 tok/s, Runs with offload (needs ~0.5 GB host RAM)
24.5 GB required23.0 GB available
107% VRAM needed

1.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.5 GB host RAM)

Decode

14.7 tok/s

TTFT

13186 ms

Safe context

14K

Memory

24.5 GB / 23.0 GB

Offload

10%

Memory breakdown

Weights7.9 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on MacBook Pro M1 Pro 32GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 14.7 tok/s decode · 13.2s TTFT (warm) · 37 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns well16.4 tok/s6442 ms14K
CodingBRuns with offload (needs ~0.5 GB host RAM)14.7 tok/s13186 ms14K
Agentic CodingFToo heavy9.0 tok/s31435 ms14K
ReasoningBRuns with offload (needs ~0.5 GB host RAM)14.7 tok/s15583 ms14K
RAGFToo heavy9.0 tok/s39294 ms14K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowA71
Q3_K_S
3
6.4 GB
LowA72
NVFP4
4
7.3 GB
MediumA72
Q4_K_M
4
7.9 GB
MediumA73
Q5_K_M
5
9.4 GB
HighA74
Q6_K
6
10.7 GB
HighA75
Q8_0Best for your GPU
8
13.9 GB
Very HighA75
F16
16
26.7 GB
MaximumF0

Get started

Copy-paste commands to run CodeLlama 13B Instruct on your machine.

Run

lms load CodeLlama-13b-Instruct-hf && lms server start

Opções de upgrade

Hardware que roda bem CodeLlama 13B Instruct

Mac mini M4 64GBOpção econômica
64 GB Unified (+32)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.9.6 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBMelhor custo-benefício
64 GB Unified (+32)273 GB/s (+73)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.23.3 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 59%.

~$1,599 MSRP

MacBook Pro M3 Pro 36GBUpgrade Apple
36 GB Unified (+4)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.13.8 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

NVIDIARTX 5090 32GBMaior salto
1792 GB/s (+1592)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.146.9 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 899%.

~$1,999 MSRP

Frequently asked questions

Can MacBook Pro M1 Pro 32GB run CodeLlama 13B Instruct?

Yes, MacBook Pro M1 Pro 32GB can run CodeLlama 13B Instruct with a B grade (Runs with offload (needs ~0.5 GB host RAM)). Expected decode speed: 14.7 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 24.5 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization for CodeLlama 13B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 13B Instruct run at on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, CodeLlama 13B Instruct achieves approximately 14.7 tokens per second decode speed with a time-to-first-token of 13186ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 32GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on MacBook Pro M1 Pro 32GB receives a B grade with 14.7 tok/s and 14K context.

What context window can CodeLlama 13B Instruct use on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, CodeLlama 13B Instruct can safely use up to 14K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if CodeLlama 13B Instruct feels slow on MacBook Pro M1 Pro 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for CodeLlama 13B Instruct?

Not always. MacBook Pro M1 Pro 32GB 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 M1 Pro 32GBSee all hardware for CodeLlama 13B Instruct
Embed this result▼

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

<iframe src="https://willitrunai.com/embed/codellama-13b-instruct-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: