RTX 3060 12GBBudget pick12 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.60.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Qwen 2.5 Math 7B needs ~7.1 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~66 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
66.1 tok/s
TTFT
2930 ms
Safe context
4K
Memory
7.1 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 | Tight fit | 66.1 tok/s | 1598 ms | 4K |
| Coding | B | Tight fit | 66.1 tok/s | 2930 ms | 4K |
| Agentic Coding | B | Runs with offload | 66.1 tok/s | 4262 ms | 4K |
| Reasoning | B | Tight fit | 66.1 tok/s | 3463 ms | 4K |
| RAG | B | Runs with offload | 66.1 tok/s | 5328 ms | 4K |
How Qwen 2.5 Math 7B (7B params) fits at each quantization level on RTX 2060 Super 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 |
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
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 48%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 12GBNVIDIA upgradeRaises estimated decode speed by about 45%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 2060 Super 8GB can run Qwen 2.5 Math 7B with a B grade (Tight fit). Expected decode speed: 66.1 tok/s.
Qwen 2.5 Math 7B (7B parameters) requires approximately 7.1 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 RTX 2060 Super 8GB, Qwen 2.5 Math 7B achieves approximately 66.1 tokens per second decode speed with a time-to-first-token of 2930ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 7B on RTX 2060 Super 8GB receives a B grade with 66.1 tok/s and 4K context.
On RTX 2060 Super 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-rtx-2060-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: