RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+72)
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 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~49 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
53.5 tok/s
TTFT
3622 ms
Safe context
4K
Memory
7.1 GB / 8.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 53.5 tok/s | 1975 ms | 4K |
| Coding | B | Tight fit | 49.2 tok/s | 3932 ms | 4K |
| Agentic Coding | B | Runs with offload | 53.5 tok/s | 5268 ms | 4K |
| Reasoning | B | Tight fit | 53.5 tok/s | 4280 ms | 4K |
| RAG | B | Runs with offload | 53.5 tok/s | 6585 ms | 4K |
How Qwen 2.5 Math 7B (7B params) fits at each quantization level on RTX 3000 Ada Laptop 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
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 83%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 12GBNVIDIA upgradeRaises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 3000 Ada Laptop 8GB can run Qwen 2.5 Math 7B with a B grade (Tight fit). Expected decode speed: 49.2 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 3000 Ada Laptop 8GB, Qwen 2.5 Math 7B achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 7B on RTX 3000 Ada Laptop 8GB receives a B grade with 49.2 tok/s and 4K context.
On RTX 3000 Ada Laptop 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-3000-ada-laptop-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 |