Can Granite Code 34B run on AMD Instinct MI60 32GB?

YES — Tight Fit

A77Great

○Estimated from fit model

Granite Code 34B needs ~28.5 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q4_K_M quantization, expect ~24 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) — 28.5 GB, 26.2 tok/s, Tight fit

28.5 GB required32.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

26.2 tok/s

TTFT

7387 ms

Safe context

Memory

28.5 GB / 32.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGranite Code 34B on AMD Instinct MI60 32GB
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: 26.2 tok/s decode · 7.4s TTFT (warm) · 66 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	A	Tight fit	24.2 tok/s	4365 ms	8K
Coding	A	Tight fit	24.2 tok/s	8002 ms	8K
Agentic Coding	A	Runs with offload	18.0 tok/s	15688 ms	8K
Reasoning	A	Tight fit	24.2 tok/s	9457 ms	8K
RAG	A	Runs with offload	18.0 tok/s	19610 ms	8K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A75
Q3_K_S	3	16.7 GB	Low	A76
NVFP4	4

Get started

Copy-paste commands to run Granite Code 34B on your machine.

Run

ollama run granite-code:34b

Your hardware

More models your AMD Instinct MI60 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	63.8 tok/s
👁 Alibaba

Frequently asked questions

See all results for AMD Instinct MI60 32GB See all hardware for Granite Code 34B

URL: https://willitrunai.com/can-run/granite-code-34b-on-instinct-mi60-32gb

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