Can DeepSeek Coder V2 236B run on AMD Instinct MI325X 256GB?

YES — Tight Fit

S89Excellent

Estimated from fit model

DeepSeek Coder V2 236B needs ~229.1 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~82 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 229.1 GB, 82.0 tok/s, Tight fit

229.1 GB required256.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

82.0 tok/s

TTFT

2362 ms

Safe context

23K

Memory

229.1 GB / 256.0 GB

Memory breakdown

Weights144.0 GB

KV Cache58.6 GB

Runtime0.9 GB

Headroom25.6 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 236B on AMD Instinct MI325X 256GB
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: 82.0 tok/s decode · 2.4s TTFT (warm) · 205 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	82.0 tok/s	1288 ms	23K
Coding	S	Tight fit	82.0 tok/s	2362 ms	23K
Agentic Coding	A	Very compromised (needs ~15.8 GB host RAM)	48.1 tok/s	5854 ms	23K
Reasoning	S	Tight fit	82.0 tok/s	2791 ms	23K
RAG	A	Very compromised (needs ~15.8 GB host RAM)	48.1 tok/s	7318 ms	23K

Inference speed

DeepSeek Coder V2 236B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek Coder V2 236B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~12 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?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	11.7	Too big
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	6.1	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	5.7	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	4.5	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.5	Too big
RTX 5090 32GB	32 GB	Q4_K_M	3.4	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	3.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	3.1	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.9	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.7	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.3	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.2	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek Coder V2 236B (236B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	A80
Q3_K_S	3	115.6 GB	Low	A82
NVFP4	4	132.2 GB	Medium	A83
Q4_K_M	4	144.0 GB	Medium	A84
Q5_K_M	5	169.9 GB	High	A84
Q6_KBest for your GPU	6	193.5 GB	High	A84
Q8_0	8	252.5 GB	Very High	F0
F16	16	483.8 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek Coder V2 236B on your machine.

Run

lms load DeepSeek-Coder-V2-Instruct && lms server start

Your hardware

More models your AMD Instinct MI325X 256GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 397B A17B	397B	A	39.2 tok/s
DeepSeek V4 Flash	284B	S	94.4 tok/s

Frequently asked questions

Can AMD Instinct MI325X 256GB run DeepSeek Coder V2 236B?

Yes, AMD Instinct MI325X 256GB can run DeepSeek Coder V2 236B with a S grade (Tight fit). Expected decode speed: 82.0 tok/s.

How much VRAM does DeepSeek Coder V2 236B need?

DeepSeek Coder V2 236B (236B parameters) requires approximately 229.1 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 AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, DeepSeek Coder V2 236B achieves approximately 82.0 tokens per second decode speed with a time-to-first-token of 2362ms using Q4_K_M quantization.

Can AMD Instinct MI325X 256GB run DeepSeek Coder V2 236B for coding?

For coding workloads, DeepSeek Coder V2 236B on AMD Instinct MI325X 256GB receives a S grade with 82.0 tok/s and 23K context.

What context window can DeepSeek Coder V2 236B use on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, DeepSeek Coder V2 236B can safely use up to 23K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI325X 256GB See all hardware for DeepSeek Coder V2 236B

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<iframe src="https://willitrunai.com/embed/deepseek-coder-v2-236b-on-instinct-mi325x-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

82.0 tok/s

1288 ms

23K

Coding

Tight fit

82.0 tok/s

2362 ms

23K

Agentic Coding

Very compromised (needs ~15.8 GB host RAM)

48.1 tok/s

5854 ms

23K

Reasoning

Tight fit

82.0 tok/s

2791 ms

23K

RAG

Very compromised (needs ~15.8 GB host RAM)

48.1 tok/s

7318 ms

23K

DeepSeek Coder V2 236B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

11.7

Too big

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

6.1

Too big

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

5.7

Too big

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

4.5

Too big

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

4.5

Too big

RTX 5090 32GB

32 GB

Q4_K_M

3.4

Too big

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

3.3

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

3.1

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

2.9

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

2.7

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

2.3

Too big

RTX 4090 24GB

24 GB

Q4_K_M

2.2

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

2.0

Too big

RTX 3090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

2.0

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

2.0

Too big

4× RTX 3060 12GB

48 GB

Q4_K_M

2.0

Too big

Quantization options

How DeepSeek Coder V2 236B (236B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	A80
Q3_K_S	3	115.6 GB	Low	A82
NVFP4	4	132.2 GB	Medium	A83
Q4_K_M	4	144.0 GB	Medium	A84
Q5_K_M	5	169.9 GB	High	A84
Q6_KBest for your GPU	6	193.5 GB	High	A84
Q8_0	8	252.5 GB	Very High	F0
F16	16	483.8 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 397B A17B

397B

39.2 tok/s

DeepSeek V4 Flash

284B

94.4 tok/s

Frequently asked questions

Can AMD Instinct MI325X 256GB run DeepSeek Coder V2 236B?

Yes, AMD Instinct MI325X 256GB can run DeepSeek Coder V2 236B with a S grade (Tight fit). Expected decode speed: 82.0 tok/s.

How much VRAM does DeepSeek Coder V2 236B need?

DeepSeek Coder V2 236B (236B parameters) requires approximately 229.1 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 AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, DeepSeek Coder V2 236B achieves approximately 82.0 tokens per second decode speed with a time-to-first-token of 2362ms using Q4_K_M quantization.

Can AMD Instinct MI325X 256GB run DeepSeek Coder V2 236B for coding?

For coding workloads, DeepSeek Coder V2 236B on AMD Instinct MI325X 256GB receives a S grade with 82.0 tok/s and 23K context.

What context window can DeepSeek Coder V2 236B use on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, DeepSeek Coder V2 236B can safely use up to 23K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.