Can DeepSeek Coder V2 16B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A75Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~26.8 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~492 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 26.8 GB, 491.8 tok/s, Runs well

26.8 GB required128.0 GB available

21% VRAM used

Fit status

Runs well

Decode

491.8 tok/s

TTFT

394 ms

Safe context

131K

Memory

26.8 GB / 128.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on Intel Data Center GPU Max 1550 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: 491.8 tok/s decode · 394ms TTFT (warm) · 1230 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	491.8 tok/s	350 ms	131K
Coding	A	Runs well	491.8 tok/s	394 ms	131K
Agentic Coding	A	Runs well	491.8 tok/s	573 ms	131K
Reasoning	A	Runs well	491.8 tok/s	465 ms	131K
RAG	A	Runs well	491.8 tok/s	716 ms	131K

Inference speed

DeepSeek Coder V2 16B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek Coder V2 16B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~293 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	292.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	186.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	168.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	159.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	149.0	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	135.9	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	113.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	107.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	83.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	83.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	58.5	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	53.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	51.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	40.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	25.5	Too big
RTX 4060 8GB	8 GB	Q4_K_M	9.6	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 16B (16B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	B67
Q3_K_S	3	7.8 GB	Low	B67
NVFP4	4	9.0 GB	Medium	B67
Q4_K_M	4	9.8 GB	Medium	B67
Q5_K_M	5	11.5 GB	High	B67
Q6_K	6	13.1 GB	High	B67
Q8_0	8	17.1 GB	Very High	B67
F16Best for your GPU	16	32.8 GB	Maximum	B69

Get started

Copy-paste commands to run DeepSeek Coder V2 16B on your machine.

Run

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

Your hardware

More models your Intel Data Center GPU Max 1550 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	304.8 tok/s
Qwen 3.5 27B	27B	S	132.2 tok/s
Qwen 3.6 27B	27B	S	82.4 tok/s
Qwen 3.5 122B A10B	122B	S	81 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run DeepSeek Coder V2 16B?

Yes, Intel Data Center GPU Max 1550 128GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 491.8 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 26.8 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 16B?

The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 16B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, DeepSeek Coder V2 16B achieves approximately 491.8 tokens per second decode speed with a time-to-first-token of 394ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on Intel Data Center GPU Max 1550 128GB receives a A grade with 491.8 tok/s and 131K context.

What context window can DeepSeek Coder V2 16B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, DeepSeek Coder V2 16B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek Coder V2 16B feels slow on Intel Data Center GPU Max 1550 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for DeepSeek Coder V2 16B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for DeepSeek Coder V2 16B

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