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
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.
Operating mode
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
Fit status
Tight fit
Decode
28.0 tok/s
TTFT
6917 ms
Safe context
4K
Memory
6.8 GB / 8.0 GB
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.
| 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 |
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 |
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
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
Raises estimated decode speed by about 135%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Raises estimated decode speed by about 81%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Yes, RX 590 8GB can run Qwen 2.5 Math 7B with a C grade (Tight fit). Expected decode speed: 28.0 tok/s.
Qwen 2.5 Math 7B (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 Math 7B is Q4_K_M, which balances quality and memory efficiency.
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.
For coding workloads, Qwen 2.5 Math 7B on RX 590 8GB receives a C grade with 28.0 tok/s and 4K context.
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.
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-590-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |