Can Qwen 2.5 Math 72B run on AMD Instinct MI210 64GB?

YES — Tight Fit

B63Good

○Estimated from fit model

Qwen 2.5 Math 72B needs ~56.1 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~28 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) — 56.1 GB, 27.6 tok/s, Tight fit

56.1 GB required64.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

27.6 tok/s

TTFT

7020 ms

Safe context

Memory

56.1 GB / 64.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Math 72B on AMD Instinct MI210 64GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 27.6 tok/s decode · 7.0s TTFT (warm) · 69 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	B	Tight fit	27.6 tok/s	3829 ms	4K
Coding	B	Tight fit	27.6 tok/s	7020 ms	4K
Agentic Coding	B	Runs with offload	27.6 tok/s	10210 ms	4K
Reasoning	B	Tight fit	27.6 tok/s	8296 ms	4K
RAG	B	Runs with offload	27.6 tok/s	12763 ms	4K

Quantization options

How Qwen 2.5 Math 72B (72B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	B60
Q3_K_S	3	35.3 GB	Low	B61
NVFP4	4	40.3 GB	Medium	B61
Q4_K_M	4	43.9 GB	Medium	B61
Q5_K_MBest for your GPU	5	51.8 GB	High	B61
Q6_K	6	59.0 GB	High	F0
Q8_0	8	77.0 GB	Very High	F0
F16	16	147.6 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Math 72B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "Qwen/Qwen2.5-Math-72B-Instruct" \
 --hf-file "Qwen2.5-Math-72B-Instruct-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options