Can Qwen 2.5 Math 7B run on RX 590 8GB?

YES — Tight Fit

C54Usable

Estimated from fit model

Qwen 2.5 Math 7B needs ~6.8 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 6.8 GB, 28.0 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

28.0 tok/s

TTFT

6917 ms

Safe context

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Math 7B on RX 590 8GB
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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	28.0 tok/s	3773 ms	4K
Coding	C	Tight fit	28.0 tok/s	6917 ms	4K
Agentic Coding	C	Runs with offload	28.0 tok/s	10061 ms	4K
Reasoning	C	Tight fit	28.0 tok/s	8174 ms	4K
RAG	C	Runs with offload	28.0 tok/s	12576 ms	4K

Quantization options

How Qwen 2.5 Math 7B (7B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B57
Q3_K_S	3	3.4 GB	Low	B58
NVFP4	4	3.9 GB	Medium	B58
Q4_K_M	4	4.3 GB	Medium	B57
Q5_K_MBest for your GPU	5	5.0 GB	High	B57
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

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

Upgrade-Optionen

Hardware, die Qwen 2.5 Math 7B gut ausführt

RX 9060 XT 16GBGünstige Wahl

16 GB VRAM (+8)320 GB/s (+64)

Raises estimated decode speed by about 83%.51.3 tok/s Dekodierung

Raises estimated decode speed by about 83%.

Adds memory headroom for longer context windows and future model growth.

ca. $349 MSRP

RX 7700 XT 12GBBestes Preis-Leistungs-Verhältnis

12 GB VRAM (+4)432 GB/s (+176)

Raises estimated decode speed by about 135%.65.9 tok/s Dekodierung

Raises estimated decode speed by about 135%.

Adds memory headroom for longer context windows and future model growth.

ca. $449 MSRP

RX 6700 XT 12GBAMD-Upgrade

12 GB VRAM (+4)384 GB/s (+128)

Raises estimated decode speed by about 81%.50.8 tok/s Dekodierung

Raises estimated decode speed by about 81%.

Adds memory headroom for longer context windows and future model growth.

ca. $479 MSRP

Frequently asked questions

Can RX 590 8GB run Qwen 2.5 Math 7B?

Yes, RX 590 8GB can run Qwen 2.5 Math 7B with a C grade (Tight fit). Expected decode speed: 28.0 tok/s.

How much VRAM does Qwen 2.5 Math 7B need?

Qwen 2.5 Math 7B (7B parameters) requires approximately 6.8 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 RX 590 8GB?

On RX 590 8GB, Qwen 2.5 Math 7B achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6917ms using Q4_K_M quantization.

Can RX 590 8GB run Qwen 2.5 Math 7B for coding?

For coding workloads, Qwen 2.5 Math 7B on RX 590 8GB receives a C grade with 28.0 tok/s and 4K context.

What context window can Qwen 2.5 Math 7B use on RX 590 8GB?

On RX 590 8GB, 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 RX 590 8GB See all hardware for Qwen 2.5 Math 7B

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