Can Qwen 2.5 Math 7B run on Radeon RX 7900M 16GB?

YES — Runs Great

B56Good

Estimated from fit model

Qwen 2.5 Math 7B needs ~7.6 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~86 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 7.6 GB, 86.4 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

86.4 tok/s

TTFT

2240 ms

Safe context

Memory

7.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Math 7B on Radeon RX 7900M 16GB
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: 86.4 tok/s decode · 2.2s TTFT (warm) · 216 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	Runs well	86.4 tok/s	1222 ms	4K
Coding	B	Runs well	86.4 tok/s	2240 ms	4K
Agentic Coding	B	Runs well	86.4 tok/s	3259 ms	4K
Reasoning	B	Runs well	86.4 tok/s	2648 ms	4K
RAG	B	Runs well	86.4 tok/s	4074 ms	4K

Inference speed

Qwen 2.5 Math 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Math 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quantization options

How Qwen 2.5 Math 7B (7B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C51
Q3_K_S	3	3.4 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Math 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "Qwen/Qwen2.5-Math-7B-Instruct" \
  --hf-file "Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can Radeon RX 7900M 16GB run Qwen 2.5 Math 7B?

Yes, Radeon RX 7900M 16GB can run Qwen 2.5 Math 7B with a B grade (Runs well). Expected decode speed: 86.4 tok/s.

How much VRAM does Qwen 2.5 Math 7B need?

Qwen 2.5 Math 7B (7B parameters) requires approximately 7.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Math 7B?

The recommended quantization for Qwen 2.5 Math 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Math 7B run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Qwen 2.5 Math 7B achieves approximately 86.4 tokens per second decode speed with a time-to-first-token of 2240ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run Qwen 2.5 Math 7B for coding?

For coding workloads, Qwen 2.5 Math 7B on Radeon RX 7900M 16GB receives a B grade with 86.4 tok/s and 4K context.

What context window can Qwen 2.5 Math 7B use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Qwen 2.5 Math 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for Qwen 2.5 Math 7B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/qwen-2.5-math-7b-on-rx-7900m-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: