Yi 34B Chat needs ~26.9 GB but Intel Arc A770 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
3.3 tok/s
TTFT
58420 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.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
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.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.8 tok/s | 27471 ms | 4K |
| Coding | F | Too heavy | 3.3 tok/s | 58420 ms | 4K |
| Agentic Coding | F | Too heavy | 2.3 tok/s | 120693 ms | 4K |
| Reasoning | F | Too heavy | 3.3 tok/s | 69042 ms | 4K |
| RAG | F | Too heavy | 2.5 tok/s | 138955 ms | 4K |
How Yi 34B Chat (34B params) fits at each quantization level on Intel Arc A770 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
Intel Arc Pro B60 24GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 124%.
~$599 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
Intel Data Center GPU Max 1550 128GBBudget 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.
~$15,000 MSRP
Gaudi 3 128GBIntel 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.
~$15,000 MSRP
No, Yi 34B Chat requires more memory than Intel Arc A770 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 Intel Arc A770 16GB, Yi 34B Chat achieves approximately 3.3 tokens per second decode speed with a time-to-first-token of 58420ms using Q4_K_M quantization.
For coding workloads, Yi 34B Chat on Intel Arc A770 16GB receives a F grade with 3.3 tok/s and 4K context.
On Intel Arc A770 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-arc-a770-16gb" 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 |
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
