WizardLM 13B needs ~22.1 GB but GTX 1070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
14.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.9 tok/s
TTFT
67763 ms
Safe context
4K
Memory
22.1 GB / 8.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 22.1 GB, but this setup only exposes 8.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.0 tok/s | 34989 ms | 4K |
| Coding | F | Too heavy | 2.9 tok/s | 67763 ms | 4K |
| Agentic Coding | F | Too heavy | 2.9 tok/s | 98565 ms | 4K |
| Reasoning | F | Too heavy | 2.9 tok/s | 80084 ms | 4K |
| RAG | F | Too heavy | 2.9 tok/s | 123206 ms | 4K |
How WizardLM 13B (13B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 5.1 GB | Low | A73 |
Q3_K_S | 3 | 6.4 GB | Low | F0 |
NVFP4 | 4 | 7.3 GB | Medium | F0 |
Q4_K_M | 4 | 7.9 GB | Medium | F0 |
Q5_K_M | 5 | 9.4 GB | High | F0 |
Q6_K | 6 | 10.7 GB | High | F0 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Upgrade-Optionen
RTX 4000 Ada 20GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 562%.
ca. $1,250 MSRP
RTX 3090 24GBGünstige WahlMakes 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.
ca. $1,499 MSRP
RTX 4090 24GBNVIDIA-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.
ca. $1,599 MSRP
No, WizardLM 13B requires more memory than GTX 1070 8GB provides.
WizardLM 13B (13B parameters) requires approximately 22.1 GB of memory with Q4_K_M quantization.
The recommended quantization for WizardLM 13B is Q4_K_M, which balances quality and memory efficiency.
On GTX 1070 8GB, WizardLM 13B achieves approximately 2.9 tokens per second decode speed with a time-to-first-token of 67763ms using Q4_K_M quantization.
For coding workloads, WizardLM 13B on GTX 1070 8GB receives a F grade with 2.9 tok/s and 4K context.
On GTX 1070 8GB, WizardLM 13B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/wizardlm-13b-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:
