RX 9060 XT 16GBBudget pick
16 GB VRAM (+8)320 GB/s (+64)
C
Raises estimated decode speed by about 83%.51.3 tok/s decode
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Can Qwen 2.5 Math 7B run on RX 580 8GB?
YES — Tight Fit
C54Usable
○Estimated from fit model
Qwen 2.5 Math 7B needs ~6.8 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Share:
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.
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
4K
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 580 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 580 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 99Upgrade options
Hardware that runs Qwen 2.5 Math 7B well
RX 7700 XT 12GBBest value
12 GB VRAM (+4)432 GB/s (+176)
B
Raises estimated decode speed by about 135%.65.9 tok/s decode
Raises estimated decode speed by about 135%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RX 6700 XT 12GBAMD upgrade
12 GB VRAM (+4)384 GB/s (+128)
B
Raises estimated decode speed by about 81%.50.8 tok/s decode
Raises estimated decode speed by about 81%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP