Yi 34B Chat needs ~26.9 GB but RTX 6000 Ada Laptop 16GB only has 16.0 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
10.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.2 tok/s
TTFT
60785 ms
Safe context
4K
Memory
26.9 GB / 16.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 26.9 GB, but this setup only exposes 16.0 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 | 3.7 tok/s | 28583 ms | 4K |
| Coding | F | Too heavy | 3.2 tok/s | 60785 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 115664 ms | 4K |
| Reasoning | F | Too heavy | 3.2 tok/s | 71837 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 144580 ms | 4K |
How Yi 34B Chat (34B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | F0 |
Q3_K_S | 3 | 16.7 GB | Low | F0 |
NVFP4 | 4 | 19.0 GB | Medium | F0 |
Q4_K_M | 4 | 20.7 GB | Medium | F0 |
Q5_K_M | 5 | 24.5 GB | High | F0 |
Q6_K | 6 | 27.9 GB | High | F0 |
Q8_0 | 8 | 36.4 GB | Very High | F0 |
F16 | 16 | 69.7 GB | Maximum | F0 |
Upgrade options
RTX 3090 24GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 269%.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 300%.
~$1,599 MSRP
RTX 5090 32GBBudget 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.
~$1,999 MSRP
No, Yi 34B Chat requires more memory than RTX 6000 Ada Laptop 16GB provides.
Yi 34B Chat (34B parameters) requires approximately 26.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.
On RTX 6000 Ada Laptop 16GB, Yi 34B Chat achieves approximately 3.2 tokens per second decode speed with a time-to-first-token of 60785ms using Q4_K_M quantization.
For coding workloads, Yi 34B Chat on RTX 6000 Ada Laptop 16GB receives a F grade with 3.2 tok/s and 4K context.
On RTX 6000 Ada Laptop 16GB, Yi 34B Chat can safely use up to 4K tokens of context. The model's official context limit is 200K, 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/yi-34b-chat-on-rtx-6000-ada-laptop-16gb" 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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