RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+104)
B
Raises estimated decode speed by about 57%.60.4 tok/s decode
Raises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Qwen 2.5 Math 7B needs ~7.1 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 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
38.4 tok/s
TTFT
5041 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 | 38.4 tok/s | 2750 ms | 4K |
| Coding | B | Tight fit | 38.4 tok/s | 5041 ms | 4K |
| Agentic Coding | B | Runs with offload | 38.4 tok/s | 7333 ms | 4K |
| Reasoning | B | Tight fit | 38.4 tok/s | 5958 ms | 4K |
| RAG | B | Runs with offload | 38.4 tok/s | 9166 ms | 4K |
How Qwen 2.5 Math 7B (7B params) fits at each quantization level on GTX 1070 Ti 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 pickRaises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA upgradeRaises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Yes, GTX 1070 Ti 8GB can run Qwen 2.5 Math 7B with a B grade (Tight fit). Expected decode speed: 38.4 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 GTX 1070 Ti 8GB, Qwen 2.5 Math 7B achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5041ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 Math 7B on GTX 1070 Ti 8GB receives a B grade with 38.4 tok/s and 4K context.
On GTX 1070 Ti 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-gtx-1070-ti-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 |