Can Qwen3-Coder-Next run on AMD Instinct MI350X 288GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Qwen3-Coder-Next needs ~80.0 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~394 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) — 80.0 GB, 394.4 tok/s, Runs well

80.0 GB required288.0 GB available

28% VRAM used

Fit status

Runs well

Decode

394.4 tok/s

TTFT

491 ms

Safe context

256K

Memory

80.0 GB / 288.0 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on AMD Instinct MI350X 288GB
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: 394.4 tok/s decode · 491ms TTFT (warm) · 986 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	394.4 tok/s	350 ms	256K
Coding	S	Runs well	394.4 tok/s	491 ms	256K
Agentic Coding	S	Runs well	394.4 tok/s	714 ms	256K
Reasoning	S	Runs well	394.4 tok/s	580 ms	256K
RAG	S	Runs well	394.4 tok/s	893 ms	256K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A78
Q3_K_S	3	39.2 GB	Low	A78
NVFP4	4	44.8 GB	Medium	A79
Q4_K_M	4	48.8 GB	Medium	A79
Q5_K_M	5	57.6 GB	High	A79
Q6_K	6	65.6 GB	High	A80
Q8_0	8	85.6 GB	Very High	A82
F16Best for your GPU	16	164.0 GB	Maximum	S87

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

Your hardware

More models your AMD Instinct MI350X 288GB can run

Model	Params	Grade	Decode
Qwen 3.5 397B A17B	397B	S	78.9 tok/s
Devstral 2 123B Instruct	123B	S	84.6 tok/s
Qwen 3.5 122B A10B	122B	S	234.8 tok/s
DeepSeek V4 Flash	284B	S	125.8 tok/s
Mistral Small 4 119B	119B	S	254.6 tok/s

Frequently asked questions

Can AMD Instinct MI350X 288GB run Qwen3-Coder-Next?

Yes, AMD Instinct MI350X 288GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 394.4 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 80.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Qwen3-Coder-Next achieves approximately 394.4 tokens per second decode speed with a time-to-first-token of 491ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on AMD Instinct MI350X 288GB receives a S grade with 394.4 tok/s and 256K context.

What context window can Qwen3-Coder-Next use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Qwen3-Coder-Next can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for Qwen3-Coder-Next

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/qwen-3-coder-next-on-instinct-mi350x-288gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: