Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for DeepSeek Coder V2 236B?

Not always. Mac Studio M1 Ultra 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 DeepSeek Coder V2 236B run on Mac Studio M1 Ultra 128GB?

Q: What should I upgrade first if DeepSeek Coder V2 236B feels slow on Mac Studio M1 Ultra 128GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek Coder V2 236B needs ~217.3 GB but Mac Studio M1 Ultra 128GB only has 92.2 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity

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) — 217.3 GB, exceeds 92.2 GB available

217.3 GB required92.2 GB available

236% VRAM needed

125.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.7 tok/s

TTFT

33709 ms

Safe context

Memory

217.3 GB / 92.2 GB

Offload

60%

Memory breakdown

Weights144.0 GB

KV Cache58.6 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek Coder V2 236B on Mac Studio M1 Ultra 128GB
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: 5.7 tok/s decode · 33.7s TTFT (warm) · 14 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 217.3 GB, but this setup only exposes 92.2 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.7 tok/s	18387 ms	4K
Coding	F	Too heavy	5.7 tok/s	33709 ms	4K
Agentic Coding	F	Too heavy	5.7 tok/s	49031 ms	4K
Reasoning	F	Too heavy	5.7 tok/s	39838 ms	4K
RAG	F	Too heavy	5.7 tok/s	61289 ms	4K

Quantization options

How DeepSeek Coder V2 236B (236B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	F0
Q3_K_S	3	115.6 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs DeepSeek Coder V2 236B well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+160)8000 GB/s (+7200)

Makes the model fit on the accelerator instead of staying completely out of reach.109.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$8,000 MSRP

AMD Instinct MI300X 192GBBest value

192 GB VRAM (+64)5300 GB/s (+4500)

Makes the model fit on the accelerator instead of staying completely out of reach.42.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 646%.

~$15,000 MSRP

Frequently asked questions

Can Mac Studio M1 Ultra 128GB run DeepSeek Coder V2 236B?

No, DeepSeek Coder V2 236B requires more memory than Mac Studio M1 Ultra 128GB provides.

How much VRAM does DeepSeek Coder V2 236B need?

DeepSeek Coder V2 236B (236B parameters) requires approximately 217.3 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 236B?

The recommended quantization for DeepSeek Coder V2 236B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 236B run at on Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, DeepSeek Coder V2 236B achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 33709ms using Q4_K_M quantization.

Can Mac Studio M1 Ultra 128GB run DeepSeek Coder V2 236B for coding?

For coding workloads, DeepSeek Coder V2 236B on Mac Studio M1 Ultra 128GB receives a F grade with 5.7 tok/s and 4K context.

What context window can DeepSeek Coder V2 236B use on Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, DeepSeek Coder V2 236B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Mac Studio M1 Ultra 128GB See all hardware for DeepSeek Coder V2 236B

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