Can Qwen3-Coder-Next run on AMD Instinct MI210 64GB?

YES — Tight Fit

S93Excellent

○Estimated from fit model

Qwen3-Coder-Next needs ~57.6 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~75 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) — 57.6 GB, 75.2 tok/s, Tight fit

57.6 GB required64.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

75.2 tok/s

TTFT

2574 ms

Safe context

86K

Memory

57.6 GB / 64.0 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on AMD Instinct MI210 64GB
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: 75.2 tok/s decode · 2.6s TTFT (warm) · 188 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	Tight fit	75.2 tok/s	1404 ms	86K
Coding	S	Tight fit	75.2 tok/s	2574 ms	86K
Agentic Coding	S	Tight fit	75.2 tok/s	3744 ms	86K
Reasoning	S	Tight fit	75.2 tok/s	3042 ms	86K
RAG	S	Tight fit	75.2 tok/s	4680 ms	86K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	S87
Q3_K_S	3	39.2 GB	Low	S88
NVFP4	4	44.8 GB	Medium	S88
Q4_K_MBest for your GPU	4	48.8 GB	Medium	S88
Q5_K_M	5	57.6 GB	High	F0
Q6_K	6	65.6 GB	High	F0
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Get started

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

Run

ollama run qwen3-coder-next

Frequently asked questions

See all results for AMD Instinct MI210 64GB See all hardware for Qwen3-Coder-Next

URL: https://willitrunai.com/can-run/qwen-3-coder-next-on-instinct-mi210-64gb

⇱ Qwen3-Coder-Next on AMD Instinct MI210 64GB? TIGHT FIT