CodeGeeX 4 9B needs ~7.7 GB but RTX 3050 Ti Laptop 4GB only has 4.0 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
3.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.6 tok/s
TTFT
34346 ms
Safe context
4K
Memory
7.7 GB / 4.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 7.7 GB, but this setup only exposes 4.0 GB of usable VRAM.
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 | 6.1 tok/s | 17205 ms | 4K |
| Coding | F | Too heavy | 5.6 tok/s | 34346 ms | 4K |
| Agentic Coding | F | Too heavy | 4.8 tok/s | 58659 ms | 4K |
| Reasoning | F | Too heavy | 5.6 tok/s | 40590 ms | 4K |
| RAG | F | Too heavy | 4.8 tok/s | 73323 ms | 4K |
How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | F0 |
Q3_K_S | 3 | 4.4 GB | Low | F0 |
NVFP4 | 4 | 5.0 GB | Medium | F0 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
アップグレードオプション
RTX 3050 8GB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.
〜$249 MSRP
RTX 5060 8GB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.
〜$299 MSRP
RTX 4060 8GBNVIDIA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.
〜$299 MSRP
RTX 3080 Ti 12GB最大の飛躍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.
〜$1,199 MSRP
No, CodeGeeX 4 9B requires more memory than RTX 3050 Ti Laptop 4GB provides.
CodeGeeX 4 9B (9B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.
The recommended quantization for CodeGeeX 4 9B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, CodeGeeX 4 9B achieves approximately 5.6 tokens per second decode speed with a time-to-first-token of 34346ms using Q4_K_M quantization.
For coding workloads, CodeGeeX 4 9B on RTX 3050 Ti Laptop 4GB receives a F grade with 5.6 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, CodeGeeX 4 9B can safely use up to 4K tokens of context. The model's official context limit is 131K, 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/codegeex-4-9b-on-rtx-3050-ti-laptop-4gb" 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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