WizardMath 7B needs ~7.9 GB VRAM. GTX 1070 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
Runs with offload
Decode
38.0 tok/s
TTFT
5091 ms
Safe context
4K
Memory
7.9 GB / 8.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 38.0 tok/s | 2777 ms | 4K |
| Coding | A | Runs with offload | 38.0 tok/s | 5091 ms | 4K |
| Agentic Coding | F | Too heavy | 17.5 tok/s | 16126 ms | 4K |
| Reasoning | A | Runs with offload | 38.0 tok/s | 6017 ms | 4K |
| RAG | F | Too heavy | 17.5 tok/s | 20157 ms | 4K |
How WizardMath 7B (7B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A74 |
Q3_K_S | 3 | 3.4 GB | Low | A74 |
NVFP4 | 4 | 3.9 GB | Medium | A74 |
Q4_K_M | 4 | 4.3 GB | Medium | A74 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | A73 |
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 WizardMath 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \
--hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | A | 15.2 tok/s | |
| Qwen 3 8B | 8B | A | 19.8 tok/s | |
 Nemotron Nano 8B | 8B | A | 21.1 tok/s | |
 InternVL2 8B | 8B | A | 21.1 tok/s | |
 Ministral 3 8B | 8B | B | 19.8 tok/s |
Yes, GTX 1070 8GB can run WizardMath 7B with a A grade (Runs with offload). Expected decode speed: 38.0 tok/s.
WizardMath 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.
The recommended quantization for WizardMath 7B is Q4_K_M, which balances quality and memory efficiency.
On GTX 1070 8GB, WizardMath 7B achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5091ms using Q4_K_M quantization.
For coding workloads, WizardMath 7B on GTX 1070 8GB receives a A grade with 38.0 tok/s and 4K context.
On GTX 1070 8GB, WizardMath 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/wizard-math-7b-on-gtx-1070-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: