WizardMath 7B needs ~7.2 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q3_K_S quantization, expect ~27 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
2.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
18.6 tok/s
TTFT
10420 ms
Safe context
4K
Memory
8.0 GB / 6.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Very compromised | 23.0 tok/s | 4595 ms | 4K |
| Coding | F | Too heavy | 17.3 tok/s | 11201 ms | 4K |
| Agentic Coding | F | Too heavy | 10.7 tok/s | 26290 ms | 4K |
| Reasoning | F | Too heavy | 17.3 tok/s | 13238 ms | 4K |
| RAG | F | Too heavy | 10.7 tok/s | 32862 ms | 4K |
How WizardMath 7B (7B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A75 |
Q3_K_SBest for your GPU | 3 | 3.4 GB | Low | A74 |
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 99Upgrade options
RTX 3050 8GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$249 MSRP
RTX 5060 8GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$299 MSRP
RTX 4060 8GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$299 MSRP
Yes, RTX 2060 6GB can run WizardMath 7B at Q3_K_S quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 8.0 GB which exceeds available memory, but at Q3_K_S it needs only 7.2 GB. Expected decode speed: 27.4 tok/s.
WizardMath 7B (7B parameters) requires approximately 8.0 GB at Q4_K_M quantization. On RTX 2060 6GB, it fits at Q3_K_S using 7.2 GB.
The recommended quantization is Q4_K_M, but on RTX 2060 6GB the best fitting quantization is Q3_K_S, which uses 7.2 GB.
On RTX 2060 6GB, WizardMath 7B achieves approximately 27.4 tokens per second decode speed with a time-to-first-token of 7059ms using Q3_K_S quantization.
For coding workloads, WizardMath 7B on RTX 2060 6GB receives a F grade with 17.3 tok/s and 4K context.
On RTX 2060 6GB, WizardMath 7B can safely use up to 4K tokens of context at Q3_K_S quantization. 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-2060-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
3.9 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 4.3 GB | Medium | F0 |
Q5_K_M | 5 | 5.0 GB | High | F0 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.