Yi 1.5 34B needs ~26.9 GB but Radeon PRO W7700 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
4.5 tok/s
TTFT
43320 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 | 5.2 tok/s | 20371 ms | 4K |
| Coding | F | Too heavy | 4.5 tok/s | 43320 ms | 4K |
| Agentic Coding | F | Too heavy | 3.4 tok/s | 82431 ms | 4K |
| Reasoning | F | Too heavy | 4.5 tok/s | 51197 ms | 4K |
| RAG | F | Too heavy | 3.4 tok/s | 103039 ms | 4K |
How Yi 1.5 34B (34B params) fits at each quantization level on Radeon PRO W7700 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 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 347%.
~$999 MSRP
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.
~$1,899 MSRP
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.
~$2,249 MSRP
NVIDIA A100 40GBBiggest leapMakes 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.
~$10,000 MSRP
No, Yi 1.5 34B requires more memory than Radeon PRO W7700 16GB provides.
Yi 1.5 34B (34B parameters) requires approximately 26.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.
On Radeon PRO W7700 16GB, Yi 1.5 34B achieves approximately 4.5 tokens per second decode speed with a time-to-first-token of 43320ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 34B on Radeon PRO W7700 16GB receives a F grade with 4.5 tok/s and 4K context.
On Radeon PRO W7700 16GB, Yi 1.5 34B can safely use up to 4K tokens of context. 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/yi-1.5-34b-on-radeon-pro-w7700-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.
