CodeLlama 7B Instruct needs ~13.9 GB but GTX 1660 Super 6GB only has 6.0 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
7.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.5 tok/s
TTFT
29822 ms
Safe context
4K
Memory
13.9 GB / 6.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.9 GB, but this setup only exposes 6.0 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 | 10.3 tok/s | 10217 ms | 4K |
| Coding | F | Too heavy | 6.5 tok/s | 29822 ms | 4K |
| Agentic Coding | F | Too heavy | 6.5 tok/s | 43378 ms | 4K |
| Reasoning | F | Too heavy | 6.5 tok/s | 35244 ms | 4K |
| RAG | F | Too heavy | 6.5 tok/s | 54222 ms | 4K |
How CodeLlama 7B Instruct (7B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A77 |
Q3_K_SBest for your GPU | 3 | 3.4 GB | Low | A77 |
NVFP4 | 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 |
アップグレードオプション
RTX 5060 Ti 16GBコスパ重視Makes 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.
〜$449 MSRP
RTX 4060 Ti 16GBコスパ最良Makes 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.
〜$499 MSRP
RTX 2000 Ada 16GBNVIDIAアップグレードMakes 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.
〜$625 MSRP
No, CodeLlama 7B Instruct requires more memory than GTX 1660 Super 6GB provides.
CodeLlama 7B Instruct (7B parameters) requires approximately 13.9 GB of memory with Q4_K_M quantization.
The recommended quantization for CodeLlama 7B Instruct is Q4_K_M, which balances quality and memory efficiency.
On GTX 1660 Super 6GB, CodeLlama 7B Instruct achieves approximately 6.5 tokens per second decode speed with a time-to-first-token of 29822ms using Q4_K_M quantization.
For coding workloads, CodeLlama 7B Instruct on GTX 1660 Super 6GB receives a F grade with 6.5 tok/s and 4K context.
On GTX 1660 Super 6GB, CodeLlama 7B Instruct can safely use up to 4K tokens of context. The model's official context limit is 16K, 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/codellama-7b-instruct-on-gtx-1660-super-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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