WizardMath 7B needs ~8.6 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q4_K_M quantization, expect ~98 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 well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
4K
Memory
8.6 GB / 12.0 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 | A | Runs well | 98.0 tok/s | 1078 ms | 4K |
| Coding | A | Runs well | 98.0 tok/s | 1976 ms | 4K |
| Agentic Coding | A | Tight fit | 98.0 tok/s | 2873 ms | 4K |
| Reasoning | A | Runs well | 98.0 tok/s | 2335 ms | 4K |
| RAG | A | Tight fit | 98.0 tok/s | 3592 ms | 4K |
How WizardMath 7B (7B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B70 |
Q3_K_S | 3 | 3.4 GB | Low | A70 |
NVFP4 | 4 |
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 | S | 126 tok/s | |
| Qwen 3 14B | 14B | A | 50.9 tok/s |
Yes, RTX 3080 Ti 12GB can run WizardMath 7B with a A grade (Runs well). Expected decode speed: 98.0 tok/s.
WizardMath 7B (7B parameters) requires approximately 8.6 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 RTX 3080 Ti 12GB, WizardMath 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, WizardMath 7B on RTX 3080 Ti 12GB receives a A grade with 98.0 tok/s and 4K context.
On RTX 3080 Ti 12GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/wizard-math-7b-on-rtx-3080-ti-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| A71 |
Q4_K_M | 4 | 4.3 GB | Medium | A72 |
Q5_K_M | 5 | 5.0 GB | High | A73 |
Q6_K | 6 | 5.7 GB | High | A73 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A72 |
F16 | 16 | 14.3 GB | Maximum | F0 |
| Qwen 3 8B | 8B | S | 112 tok/s |
 Nemotron Nano 8B | 8B | S | 112 tok/s |
 Ministral 3 14B | 14B | A | 50.7 tok/s |