Can InternLM 20B run on AMD Instinct MI60 32GB?

BARELY — Tight on Memory

C47Usable

○Estimated from fit model

InternLM 20B needs ~36.8 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q5_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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

Q5_K_M (High quality) — 36.8 GB, 19.9 tok/s, Very compromised (needs ~1.9 GB host RAM)

36.8 GB required32.0 GB available

115% VRAM needed

4.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.9 GB host RAM)

Decode

19.9 tok/s

TTFT

9753 ms

Safe context

Memory

36.8 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsInternLM 20B 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: 19.9 tok/s decode · 9.8s TTFT (warm) · 50 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	35.5 tok/s	2971 ms	8K
Coding	C	Very compromised (needs ~1.9 GB host RAM)	19.9 tok/s	9753 ms	8K
Agentic Coding	F	Too heavy	8.5 tok/s	33187 ms	8K
Reasoning	C	Very compromised (needs ~1.9 GB host RAM)	19.9 tok/s	11526 ms	8K
RAG	F	Too heavy	8.5 tok/s	41484 ms	8K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C53
Q3_K_S	3	9.8 GB	Low	C54
NVFP4	4	11.2 GB	Medium	C54
Q4_K_M	4	12.2 GB	Medium	C55
Q5_K_M	5	14.4 GB	High	B56
Q6_K	6	16.4 GB	High	B57
Q8_0Best for your GPU	8	21.4 GB	Very High	B57
F16	16	41.0 GB	Maximum	F0

Get started

Copy-paste commands to run InternLM 20B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "internlm/internlm2_5-20b-chat" \
 --hf-file "internlm2_5-20b-chat-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options